Initial deployment with all files and images

.dockerignore

@@ -0,0 +1,7 @@
+__pycache__
+*.pyc
+.git
+.gitignore
+.env
+*.md
+!README.md 

.gitattributes

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Crop Classification With Recommendation System-checkpoint.ipynb

@@ -0,0 +1,1907 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bdfd636",
+   "metadata": {},
+   "source": [
+    "# Import Libaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bee9b73",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\computation\\expressions.py:21: UserWarning: Pandas requires version '2.8.4' or newer of 'numexpr' (version '2.8.1' currently installed).\n",
+      "  from pandas.core.computation.check import NUMEXPR_INSTALLED\n",
+      "C:\\Users\\Noor Saeed\\anaconda3\\lib\\site-packages\\pandas\\core\\arrays\\masked.py:60: UserWarning: Pandas requires version '1.3.6' or newer of 'bottleneck' (version '1.3.4' currently installed).\n",
+      "  from pandas.core import (\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2822305c",
+   "metadata": {},
+   "source": [
+    "# Importing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6f8884",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    N   P   K  temperature   humidity        ph    rainfall label\n",
+       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
+       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
+       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
+       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
+       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop = pd.read_csv(\"Crop_recommendation.csv\")\n",
+    "crop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ddfb22",
+   "metadata": {},
+   "source": [
+    "# Asq Six Question to yourself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ca70c00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2200, 8)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2ae9b60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2200 entries, 0 to 2199\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   N            2200 non-null   int64  \n",
+      " 1   P            2200 non-null   int64  \n",
+      " 2   K            2200 non-null   int64  \n",
+      " 3   temperature  2200 non-null   float64\n",
+      " 4   humidity     2200 non-null   float64\n",
+      " 5   ph           2200 non-null   float64\n",
+      " 6   rainfall     2200 non-null   float64\n",
+      " 7   label        2200 non-null   object \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 137.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "crop.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9efad4c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "N              0\n",
+       "P              0\n",
+       "K              0\n",
+       "temperature    0\n",
+       "humidity       0\n",
+       "ph             0\n",
+       "rainfall       0\n",
+       "label          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1f7bf8c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3d5b7413",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.551818</td>\n",
+       "      <td>53.362727</td>\n",
+       "      <td>48.149091</td>\n",
+       "      <td>25.616244</td>\n",
+       "      <td>71.481779</td>\n",
+       "      <td>6.469480</td>\n",
+       "      <td>103.463655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>36.917334</td>\n",
+       "      <td>32.985883</td>\n",
+       "      <td>50.647931</td>\n",
+       "      <td>5.063749</td>\n",
+       "      <td>22.263812</td>\n",
+       "      <td>0.773938</td>\n",
+       "      <td>54.958389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.825675</td>\n",
+       "      <td>14.258040</td>\n",
+       "      <td>3.504752</td>\n",
+       "      <td>20.211267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>22.769375</td>\n",
+       "      <td>60.261953</td>\n",
+       "      <td>5.971693</td>\n",
+       "      <td>64.551686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>25.598693</td>\n",
+       "      <td>80.473146</td>\n",
+       "      <td>6.425045</td>\n",
+       "      <td>94.867624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>84.250000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>28.561654</td>\n",
+       "      <td>89.948771</td>\n",
+       "      <td>6.923643</td>\n",
+       "      <td>124.267508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>43.675493</td>\n",
+       "      <td>99.981876</td>\n",
+       "      <td>9.935091</td>\n",
+       "      <td>298.560117</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 N            P            K  temperature     humidity  \\\n",
+       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
+       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
+       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
+       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
+       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
+       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
+       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
+       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
+       "\n",
+       "                ph     rainfall  \n",
+       "count  2200.000000  2200.000000  \n",
+       "mean      6.469480   103.463655  \n",
+       "std       0.773938    54.958389  \n",
+       "min       3.504752    20.211267  \n",
+       "25%       5.971693    64.551686  \n",
+       "50%       6.425045    94.867624  \n",
+       "75%       6.923643   124.267508  \n",
+       "max       9.935091   298.560117  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "1056bfba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "label\n",
+       "rice           100\n",
+       "maize          100\n",
+       "jute           100\n",
+       "cotton         100\n",
+       "coconut        100\n",
+       "papaya         100\n",
+       "orange         100\n",
+       "apple          100\n",
+       "muskmelon      100\n",
+       "watermelon     100\n",
+       "grapes         100\n",
+       "mango          100\n",
+       "banana         100\n",
+       "pomegranate    100\n",
+       "lentil         100\n",
+       "blackgram      100\n",
+       "mungbean       100\n",
+       "mothbeans      100\n",
+       "pigeonpeas     100\n",
+       "kidneybeans    100\n",
+       "chickpea       100\n",
+       "coffee         100\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e3af150",
+   "metadata": {},
+   "source": [
+    "# Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8c35d395",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crop_dict = {\n",
+    "    'rice': 1,\n",
+    "    'maize': 2,\n",
+    "    'jute': 3,\n",
+    "    'cotton': 4,\n",
+    "    'coconut': 5,\n",
+    "    'papaya': 6,\n",
+    "    'orange': 7,\n",
+    "    'apple': 8,\n",
+    "    'muskmelon': 9,\n",
+    "    'watermelon': 10,\n",
+    "    'grapes': 11,\n",
+    "    'mango': 12,\n",
+    "    'banana': 13,\n",
+    "    'pomegranate': 14,\n",
+    "    'lentil': 15,\n",
+    "    'blackgram': 16,\n",
+    "    'mungbean': 17,\n",
+    "    'mothbeans': 18,\n",
+    "    'pigeonpeas': 19,\n",
+    "    'kidneybeans': 20,\n",
+    "    'chickpea': 21,\n",
+    "    'coffee': 22\n",
+    "}\n",
+    "crop['crop_num']=  crop['label'].map(crop_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b1a53f7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "      <th>crop_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2195</th>\n",
+       "      <td>107</td>\n",
+       "      <td>34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.774637</td>\n",
+       "      <td>66.413269</td>\n",
+       "      <td>6.780064</td>\n",
+       "      <td>177.774507</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2196</th>\n",
+       "      <td>99</td>\n",
+       "      <td>15</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27.417112</td>\n",
+       "      <td>56.636362</td>\n",
+       "      <td>6.086922</td>\n",
+       "      <td>127.924610</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2197</th>\n",
+       "      <td>118</td>\n",
+       "      <td>33</td>\n",
+       "      <td>30</td>\n",
+       "      <td>24.131797</td>\n",
+       "      <td>67.225123</td>\n",
+       "      <td>6.362608</td>\n",
+       "      <td>173.322839</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2198</th>\n",
+       "      <td>117</td>\n",
+       "      <td>32</td>\n",
+       "      <td>34</td>\n",
+       "      <td>26.272418</td>\n",
+       "      <td>52.127394</td>\n",
+       "      <td>6.758793</td>\n",
+       "      <td>127.175293</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23.603016</td>\n",
+       "      <td>60.396475</td>\n",
+       "      <td>6.779833</td>\n",
+       "      <td>140.937041</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2200 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N   P   K  temperature   humidity        ph    rainfall   label  \\\n",
+       "0      90  42  43    20.879744  82.002744  6.502985  202.935536    rice   \n",
+       "1      85  58  41    21.770462  80.319644  7.038096  226.655537    rice   \n",
+       "2      60  55  44    23.004459  82.320763  7.840207  263.964248    rice   \n",
+       "3      74  35  40    26.491096  80.158363  6.980401  242.864034    rice   \n",
+       "4      78  42  42    20.130175  81.604873  7.628473  262.717340    rice   \n",
+       "...   ...  ..  ..          ...        ...       ...         ...     ...   \n",
+       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee   \n",
+       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee   \n",
+       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee   \n",
+       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee   \n",
+       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee   \n",
+       "\n",
+       "      crop_num  \n",
+       "0            1  \n",
+       "1            1  \n",
+       "2            1  \n",
+       "3            1  \n",
+       "4            1  \n",
+       "...        ...  \n",
+       "2195        22  \n",
+       "2196        22  \n",
+       "2197        22  \n",
+       "2198        22  \n",
+       "2199        22  \n",
+       "\n",
+       "[2200 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "dff5caca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>crop_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    N   P   K  temperature   humidity        ph    rainfall  crop_num\n",
+       "0  90  42  43    20.879744  82.002744  6.502985  202.935536         1\n",
+       "1  85  58  41    21.770462  80.319644  7.038096  226.655537         1\n",
+       "2  60  55  44    23.004459  82.320763  7.840207  263.964248         1\n",
+       "3  74  35  40    26.491096  80.158363  6.980401  242.864034         1\n",
+       "4  78  42  42    20.130175  81.604873  7.628473  262.717340         1"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.drop(['label'],axis=1,inplace=True)\n",
+    "crop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5494675",
+   "metadata": {},
+   "source": [
+    "# Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "5a049f55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = crop.drop(['crop_num'],axis=1)\n",
+    "y = crop['crop_num']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "9d223a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2195</th>\n",
+       "      <td>107</td>\n",
+       "      <td>34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.774637</td>\n",
+       "      <td>66.413269</td>\n",
+       "      <td>6.780064</td>\n",
+       "      <td>177.774507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2196</th>\n",
+       "      <td>99</td>\n",
+       "      <td>15</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27.417112</td>\n",
+       "      <td>56.636362</td>\n",
+       "      <td>6.086922</td>\n",
+       "      <td>127.924610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2197</th>\n",
+       "      <td>118</td>\n",
+       "      <td>33</td>\n",
+       "      <td>30</td>\n",
+       "      <td>24.131797</td>\n",
+       "      <td>67.225123</td>\n",
+       "      <td>6.362608</td>\n",
+       "      <td>173.322839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2198</th>\n",
+       "      <td>117</td>\n",
+       "      <td>32</td>\n",
+       "      <td>34</td>\n",
+       "      <td>26.272418</td>\n",
+       "      <td>52.127394</td>\n",
+       "      <td>6.758793</td>\n",
+       "      <td>127.175293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23.603016</td>\n",
+       "      <td>60.396475</td>\n",
+       "      <td>6.779833</td>\n",
+       "      <td>140.937041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2200 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N   P   K  temperature   humidity        ph    rainfall\n",
+       "0      90  42  43    20.879744  82.002744  6.502985  202.935536\n",
+       "1      85  58  41    21.770462  80.319644  7.038096  226.655537\n",
+       "2      60  55  44    23.004459  82.320763  7.840207  263.964248\n",
+       "3      74  35  40    26.491096  80.158363  6.980401  242.864034\n",
+       "4      78  42  42    20.130175  81.604873  7.628473  262.717340\n",
+       "...   ...  ..  ..          ...        ...       ...         ...\n",
+       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507\n",
+       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610\n",
+       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839\n",
+       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293\n",
+       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041\n",
+       "\n",
+       "[2200 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "d2601fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "        ..\n",
+       "2195    22\n",
+       "2196    22\n",
+       "2197    22\n",
+       "2198    22\n",
+       "2199    22\n",
+       "Name: crop_num, Length: 2200, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "c561ea31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2200,)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "caba8efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "6774a9dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "41b6bcbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1656</th>\n",
+       "      <td>17</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "      <td>16.396243</td>\n",
+       "      <td>92.181519</td>\n",
+       "      <td>6.625539</td>\n",
+       "      <td>102.944161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>37</td>\n",
+       "      <td>79</td>\n",
+       "      <td>19</td>\n",
+       "      <td>27.543848</td>\n",
+       "      <td>69.347863</td>\n",
+       "      <td>7.143943</td>\n",
+       "      <td>69.408782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>7</td>\n",
+       "      <td>73</td>\n",
+       "      <td>25</td>\n",
+       "      <td>27.521856</td>\n",
+       "      <td>63.132153</td>\n",
+       "      <td>7.288057</td>\n",
+       "      <td>45.208411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1041</th>\n",
+       "      <td>101</td>\n",
+       "      <td>70</td>\n",
+       "      <td>48</td>\n",
+       "      <td>25.360592</td>\n",
+       "      <td>75.031933</td>\n",
+       "      <td>6.012697</td>\n",
+       "      <td>116.553145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1179</th>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>30</td>\n",
+       "      <td>35.474783</td>\n",
+       "      <td>47.972305</td>\n",
+       "      <td>6.279134</td>\n",
+       "      <td>97.790725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1638</th>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>21.213070</td>\n",
+       "      <td>91.353492</td>\n",
+       "      <td>7.817846</td>\n",
+       "      <td>112.983436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1095</th>\n",
+       "      <td>108</td>\n",
+       "      <td>94</td>\n",
+       "      <td>47</td>\n",
+       "      <td>27.359116</td>\n",
+       "      <td>84.546250</td>\n",
+       "      <td>6.387431</td>\n",
+       "      <td>90.812505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1130</th>\n",
+       "      <td>11</td>\n",
+       "      <td>36</td>\n",
+       "      <td>31</td>\n",
+       "      <td>27.920633</td>\n",
+       "      <td>51.779659</td>\n",
+       "      <td>6.475449</td>\n",
+       "      <td>100.258567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>11</td>\n",
+       "      <td>124</td>\n",
+       "      <td>204</td>\n",
+       "      <td>13.429886</td>\n",
+       "      <td>80.066340</td>\n",
+       "      <td>6.361141</td>\n",
+       "      <td>71.400430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>860</th>\n",
+       "      <td>32</td>\n",
+       "      <td>78</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23.970814</td>\n",
+       "      <td>62.355576</td>\n",
+       "      <td>7.007038</td>\n",
+       "      <td>53.409060</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1760 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N    P    K  temperature   humidity        ph    rainfall\n",
+       "1656   17   16   14    16.396243  92.181519  6.625539  102.944161\n",
+       "752    37   79   19    27.543848  69.347863  7.143943   69.408782\n",
+       "892     7   73   25    27.521856  63.132153  7.288057   45.208411\n",
+       "1041  101   70   48    25.360592  75.031933  6.012697  116.553145\n",
+       "1179    0   17   30    35.474783  47.972305  6.279134   97.790725\n",
+       "...   ...  ...  ...          ...        ...       ...         ...\n",
+       "1638   10    5    5    21.213070  91.353492  7.817846  112.983436\n",
+       "1095  108   94   47    27.359116  84.546250  6.387431   90.812505\n",
+       "1130   11   36   31    27.920633  51.779659  6.475449  100.258567\n",
+       "1294   11  124  204    13.429886  80.066340  6.361141   71.400430\n",
+       "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
+       "\n",
+       "[1760 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab13cdf8",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Scale the features using MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f19981a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "ms = MinMaxScaler()\n",
+    "\n",
+    "X_train = ms.fit_transform(X_train)\n",
+    "X_test = ms.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "f3f50c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.12142857, 0.07857143, 0.045     , ..., 0.9089898 , 0.48532225,\n",
+       "        0.29685161],\n",
+       "       [0.26428571, 0.52857143, 0.07      , ..., 0.64257946, 0.56594073,\n",
+       "        0.17630752],\n",
+       "       [0.05      , 0.48571429, 0.1       , ..., 0.57005802, 0.58835229,\n",
+       "        0.08931844],\n",
+       "       ...,\n",
+       "       [0.07857143, 0.22142857, 0.13      , ..., 0.43760347, 0.46198144,\n",
+       "        0.28719815],\n",
+       "       [0.07857143, 0.85      , 0.995     , ..., 0.76763665, 0.44420505,\n",
+       "        0.18346657],\n",
+       "       [0.22857143, 0.52142857, 0.085     , ..., 0.56099735, 0.54465022,\n",
+       "        0.11879596]])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "752a08ae",
+   "metadata": {},
+   "source": [
+    "# Training Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "ac6ef55e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Logistic Regression  with accuracy : 0.9181818181818182\n",
+      "Confusion matrix :  [[16  0  3  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 6  0 16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 3  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  3  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3 17  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 10  0  0  1  0  0 13  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  2  0  0 18  2  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Naive Bayes  with accuracy : 0.9954545454545455\n",
+      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 24  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Support Vector Machine  with accuracy : 0.9681818181818181\n",
+      "Confusion matrix :  [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "K-Nearest Neighbors  with accuracy : 0.9704545454545455\n",
+      "Confusion matrix :  [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 1  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Decision Tree  with accuracy : 0.9818181818181818\n",
+      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 3  0 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0 22  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Random Forest  with accuracy : 0.9931818181818182\n",
+      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 23  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Bagging  with accuracy : 0.9886363636363636\n",
+      "Confusion matrix :  [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 1  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0 22  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Noor Saeed\\AppData\\Roaming\\Python\\Python39\\site-packages\\sklearn\\ensemble\\_weight_boosting.py:527: FutureWarning: The SAMME.R algorithm (the default) is deprecated and will be removed in 1.6. Use the SAMME algorithm to circumvent this warning.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "AdaBoost  with accuracy : 0.09545454545454546\n",
+      "Confusion matrix :  [[ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 27  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 24  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0]]\n",
+      "==========================================================\n",
+      "Gradient Boosting  with accuracy : 0.9818181818181818\n",
+      "Confusion matrix :  [[15  0  4  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  1  0 26  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 23  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 22  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Extra Trees  with accuracy : 0.8863636363636364\n",
+      "Confusion matrix :  [[12  0  7  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 18  0  2  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0]\n",
+      " [ 6  0 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 2  0  0  0  0 20  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 16  1  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 10  0  0  1  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 15  0  3  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  2 17  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  1  2  2 17  1  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  2  0  0 16  4  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  4 15  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  0  0  0 16]]\n",
+      "==========================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.tree import ExtraTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.ensemble import BaggingClassifier\n",
+    "from sklearn.ensemble import GradientBoostingClassifier\n",
+    "from sklearn.ensemble import AdaBoostClassifier\n",
+    "from sklearn.metrics import accuracy_score,confusion_matrix\n",
+    "\n",
+    "# create instances of all models\n",
+    "models = {\n",
+    "    'Logistic Regression': LogisticRegression(),\n",
+    "    'Naive Bayes': GaussianNB(),\n",
+    "    'Support Vector Machine': SVC(),\n",
+    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
+    "    'Decision Tree': DecisionTreeClassifier(),\n",
+    "    'Random Forest': RandomForestClassifier(),\n",
+    "    'Bagging': BaggingClassifier(),\n",
+    "    'AdaBoost': AdaBoostClassifier(),\n",
+    "    'Gradient Boosting': GradientBoostingClassifier(),\n",
+    "    'Extra Trees': ExtraTreeClassifier(),\n",
+    "}\n",
+    "\n",
+    "\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train,y_train)\n",
+    "    ypred = model.predict(X_test)\n",
+    "    \n",
+    "    print(f\"{name}  with accuracy : {accuracy_score(y_test,ypred)}\")\n",
+    "    print(\"Confusion matrix : \",confusion_matrix(y_test,ypred))\n",
+    "    print(\"==========================================================\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "4659be4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9931818181818182"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# finally selected randomforest model\n",
+    "\n",
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train,y_train)\n",
+    "ypred = rfc.predict(X_test)\n",
+    "accuracy_score(y_test,ypred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "3e72d7f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9954545454545455"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# or gausianjb\n",
+    "gnb = GaussianNB()\n",
+    "gnb.fit(X_train,y_train)\n",
+    "ypred = gnb.predict(X_test)\n",
+    "accuracy_score(y_test,ypred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859d9922",
+   "metadata": {},
+   "source": [
+    "# Predictive System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "17f3a3fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommendation(N,P,k,temperature,humidity,ph,rainfal):\n",
+    "    features = np.array([[N,P,k,temperature,humidity,ph,rainfal]])\n",
+    "    transformed_features = ms.fit_transform(features)\n",
+    "    prediction = rfc.predict(transformed_features)\n",
+    "    print(prediction)\n",
+    "    return prediction[0] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "64ffd9d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9]\n",
+      "Muskmelon is a best crop to be cultivated \n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs\n",
+    "\n",
+    "\n",
+    "N = 40\n",
+    "P = 50\n",
+    "k = 50\n",
+    "temperature = 40.0\n",
+    "humidity = 20\n",
+    "ph = 100\n",
+    "rainfall = 100\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "2ea8ffda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9]\n",
+      "Muskmelon is a best crop to be cultivated \n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs 2\n",
+    "\n",
+    "\n",
+    "N = 100\n",
+    "P = 90\n",
+    "k = 100\n",
+    "temperature = 50.0\n",
+    "humidity = 90.0\n",
+    "ph = 100\n",
+    "rainfall = 202.0\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "d0dccd4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[9]\n",
+      "Muskmelon is a best crop to be cultivated \n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs 2\n",
+    "N = 10\n",
+    "P = 10\n",
+    "k = 10\n",
+    "temperature = 15.0\n",
+    "humidity = 80.0\n",
+    "ph = 4.5\n",
+    "rainfall = 10.0\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "fa3d3b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "pickle.dump(rfc,open('model.pkl','wb'))\n",
+    "pickle.dump(ms,open('minmaxscaler.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a55a48a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c97733fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Crop Classification With Recommendation System.ipynb

@@ -0,0 +1,1795 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6bdfd636",
+   "metadata": {},
+   "source": [
+    "# Import Libaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bee9b73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2822305c",
+   "metadata": {},
+   "source": [
+    "# Importing Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5b6f8884",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    N   P   K  temperature   humidity        ph    rainfall label\n",
+       "0  90  42  43    20.879744  82.002744  6.502985  202.935536  rice\n",
+       "1  85  58  41    21.770462  80.319644  7.038096  226.655537  rice\n",
+       "2  60  55  44    23.004459  82.320763  7.840207  263.964248  rice\n",
+       "3  74  35  40    26.491096  80.158363  6.980401  242.864034  rice\n",
+       "4  78  42  42    20.130175  81.604873  7.628473  262.717340  rice"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop = pd.read_csv(\"Crop_recommendation.csv\")\n",
+    "crop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e9ddfb22",
+   "metadata": {},
+   "source": [
+    "# Asq Six Question to yourself"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3ca70c00",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2200, 8)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e2ae9b60",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 2200 entries, 0 to 2199\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   N            2200 non-null   int64  \n",
+      " 1   P            2200 non-null   int64  \n",
+      " 2   K            2200 non-null   int64  \n",
+      " 3   temperature  2200 non-null   float64\n",
+      " 4   humidity     2200 non-null   float64\n",
+      " 5   ph           2200 non-null   float64\n",
+      " 6   rainfall     2200 non-null   float64\n",
+      " 7   label        2200 non-null   object \n",
+      "dtypes: float64(4), int64(3), object(1)\n",
+      "memory usage: 137.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "crop.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9efad4c4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "N              0\n",
+       "P              0\n",
+       "K              0\n",
+       "temperature    0\n",
+       "humidity       0\n",
+       "ph             0\n",
+       "rainfall       0\n",
+       "label          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1f7bf8c5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.duplicated().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3d5b7413",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "      <td>2200.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.551818</td>\n",
+       "      <td>53.362727</td>\n",
+       "      <td>48.149091</td>\n",
+       "      <td>25.616244</td>\n",
+       "      <td>71.481779</td>\n",
+       "      <td>6.469480</td>\n",
+       "      <td>103.463655</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>36.917334</td>\n",
+       "      <td>32.985883</td>\n",
+       "      <td>50.647931</td>\n",
+       "      <td>5.063749</td>\n",
+       "      <td>22.263812</td>\n",
+       "      <td>0.773938</td>\n",
+       "      <td>54.958389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>8.825675</td>\n",
+       "      <td>14.258040</td>\n",
+       "      <td>3.504752</td>\n",
+       "      <td>20.211267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>20.000000</td>\n",
+       "      <td>22.769375</td>\n",
+       "      <td>60.261953</td>\n",
+       "      <td>5.971693</td>\n",
+       "      <td>64.551686</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>51.000000</td>\n",
+       "      <td>32.000000</td>\n",
+       "      <td>25.598693</td>\n",
+       "      <td>80.473146</td>\n",
+       "      <td>6.425045</td>\n",
+       "      <td>94.867624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>84.250000</td>\n",
+       "      <td>68.000000</td>\n",
+       "      <td>49.000000</td>\n",
+       "      <td>28.561654</td>\n",
+       "      <td>89.948771</td>\n",
+       "      <td>6.923643</td>\n",
+       "      <td>124.267508</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>140.000000</td>\n",
+       "      <td>145.000000</td>\n",
+       "      <td>205.000000</td>\n",
+       "      <td>43.675493</td>\n",
+       "      <td>99.981876</td>\n",
+       "      <td>9.935091</td>\n",
+       "      <td>298.560117</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 N            P            K  temperature     humidity  \\\n",
+       "count  2200.000000  2200.000000  2200.000000  2200.000000  2200.000000   \n",
+       "mean     50.551818    53.362727    48.149091    25.616244    71.481779   \n",
+       "std      36.917334    32.985883    50.647931     5.063749    22.263812   \n",
+       "min       0.000000     5.000000     5.000000     8.825675    14.258040   \n",
+       "25%      21.000000    28.000000    20.000000    22.769375    60.261953   \n",
+       "50%      37.000000    51.000000    32.000000    25.598693    80.473146   \n",
+       "75%      84.250000    68.000000    49.000000    28.561654    89.948771   \n",
+       "max     140.000000   145.000000   205.000000    43.675493    99.981876   \n",
+       "\n",
+       "                ph     rainfall  \n",
+       "count  2200.000000  2200.000000  \n",
+       "mean      6.469480   103.463655  \n",
+       "std       0.773938    54.958389  \n",
+       "min       3.504752    20.211267  \n",
+       "25%       5.971693    64.551686  \n",
+       "50%       6.425045    94.867624  \n",
+       "75%       6.923643   124.267508  \n",
+       "max       9.935091   298.560117  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1056bfba",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "label\n",
+       "rice           100\n",
+       "maize          100\n",
+       "jute           100\n",
+       "cotton         100\n",
+       "coconut        100\n",
+       "papaya         100\n",
+       "orange         100\n",
+       "apple          100\n",
+       "muskmelon      100\n",
+       "watermelon     100\n",
+       "grapes         100\n",
+       "mango          100\n",
+       "banana         100\n",
+       "pomegranate    100\n",
+       "lentil         100\n",
+       "blackgram      100\n",
+       "mungbean       100\n",
+       "mothbeans      100\n",
+       "pigeonpeas     100\n",
+       "kidneybeans    100\n",
+       "chickpea       100\n",
+       "coffee         100\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop['label'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e3af150",
+   "metadata": {},
+   "source": [
+    "# Encoding"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8c35d395",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crop_dict = {\n",
+    "    'rice': 1,\n",
+    "    'maize': 2,\n",
+    "    'jute': 3,\n",
+    "    'cotton': 4,\n",
+    "    'coconut': 5,\n",
+    "    'papaya': 6,\n",
+    "    'orange': 7,\n",
+    "    'apple': 8,\n",
+    "    'muskmelon': 9,\n",
+    "    'watermelon': 10,\n",
+    "    'grapes': 11,\n",
+    "    'mango': 12,\n",
+    "    'banana': 13,\n",
+    "    'pomegranate': 14,\n",
+    "    'lentil': 15,\n",
+    "    'blackgram': 16,\n",
+    "    'mungbean': 17,\n",
+    "    'mothbeans': 18,\n",
+    "    'pigeonpeas': 19,\n",
+    "    'kidneybeans': 20,\n",
+    "    'chickpea': 21,\n",
+    "    'coffee': 22\n",
+    "}\n",
+    "crop['crop_num']=  crop['label'].map(crop_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b1a53f7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>label</th>\n",
+       "      <th>crop_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>rice</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2195</th>\n",
+       "      <td>107</td>\n",
+       "      <td>34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.774637</td>\n",
+       "      <td>66.413269</td>\n",
+       "      <td>6.780064</td>\n",
+       "      <td>177.774507</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2196</th>\n",
+       "      <td>99</td>\n",
+       "      <td>15</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27.417112</td>\n",
+       "      <td>56.636362</td>\n",
+       "      <td>6.086922</td>\n",
+       "      <td>127.924610</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2197</th>\n",
+       "      <td>118</td>\n",
+       "      <td>33</td>\n",
+       "      <td>30</td>\n",
+       "      <td>24.131797</td>\n",
+       "      <td>67.225123</td>\n",
+       "      <td>6.362608</td>\n",
+       "      <td>173.322839</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2198</th>\n",
+       "      <td>117</td>\n",
+       "      <td>32</td>\n",
+       "      <td>34</td>\n",
+       "      <td>26.272418</td>\n",
+       "      <td>52.127394</td>\n",
+       "      <td>6.758793</td>\n",
+       "      <td>127.175293</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23.603016</td>\n",
+       "      <td>60.396475</td>\n",
+       "      <td>6.779833</td>\n",
+       "      <td>140.937041</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2200 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N   P   K  temperature   humidity        ph    rainfall   label  \\\n",
+       "0      90  42  43    20.879744  82.002744  6.502985  202.935536    rice   \n",
+       "1      85  58  41    21.770462  80.319644  7.038096  226.655537    rice   \n",
+       "2      60  55  44    23.004459  82.320763  7.840207  263.964248    rice   \n",
+       "3      74  35  40    26.491096  80.158363  6.980401  242.864034    rice   \n",
+       "4      78  42  42    20.130175  81.604873  7.628473  262.717340    rice   \n",
+       "...   ...  ..  ..          ...        ...       ...         ...     ...   \n",
+       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507  coffee   \n",
+       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610  coffee   \n",
+       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839  coffee   \n",
+       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293  coffee   \n",
+       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041  coffee   \n",
+       "\n",
+       "      crop_num  \n",
+       "0            1  \n",
+       "1            1  \n",
+       "2            1  \n",
+       "3            1  \n",
+       "4            1  \n",
+       "...        ...  \n",
+       "2195        22  \n",
+       "2196        22  \n",
+       "2197        22  \n",
+       "2198        22  \n",
+       "2199        22  \n",
+       "\n",
+       "[2200 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "dff5caca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "      <th>crop_num</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    N   P   K  temperature   humidity        ph    rainfall  crop_num\n",
+       "0  90  42  43    20.879744  82.002744  6.502985  202.935536         1\n",
+       "1  85  58  41    21.770462  80.319644  7.038096  226.655537         1\n",
+       "2  60  55  44    23.004459  82.320763  7.840207  263.964248         1\n",
+       "3  74  35  40    26.491096  80.158363  6.980401  242.864034         1\n",
+       "4  78  42  42    20.130175  81.604873  7.628473  262.717340         1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "crop.drop(['label'],axis=1,inplace=True)\n",
+    "crop.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5494675",
+   "metadata": {},
+   "source": [
+    "# Train Test Split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5a049f55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = crop.drop(['crop_num'],axis=1)\n",
+    "y = crop['crop_num']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "9d223a69",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>90</td>\n",
+       "      <td>42</td>\n",
+       "      <td>43</td>\n",
+       "      <td>20.879744</td>\n",
+       "      <td>82.002744</td>\n",
+       "      <td>6.502985</td>\n",
+       "      <td>202.935536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>85</td>\n",
+       "      <td>58</td>\n",
+       "      <td>41</td>\n",
+       "      <td>21.770462</td>\n",
+       "      <td>80.319644</td>\n",
+       "      <td>7.038096</td>\n",
+       "      <td>226.655537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>60</td>\n",
+       "      <td>55</td>\n",
+       "      <td>44</td>\n",
+       "      <td>23.004459</td>\n",
+       "      <td>82.320763</td>\n",
+       "      <td>7.840207</td>\n",
+       "      <td>263.964248</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>74</td>\n",
+       "      <td>35</td>\n",
+       "      <td>40</td>\n",
+       "      <td>26.491096</td>\n",
+       "      <td>80.158363</td>\n",
+       "      <td>6.980401</td>\n",
+       "      <td>242.864034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>78</td>\n",
+       "      <td>42</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.130175</td>\n",
+       "      <td>81.604873</td>\n",
+       "      <td>7.628473</td>\n",
+       "      <td>262.717340</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2195</th>\n",
+       "      <td>107</td>\n",
+       "      <td>34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>26.774637</td>\n",
+       "      <td>66.413269</td>\n",
+       "      <td>6.780064</td>\n",
+       "      <td>177.774507</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2196</th>\n",
+       "      <td>99</td>\n",
+       "      <td>15</td>\n",
+       "      <td>27</td>\n",
+       "      <td>27.417112</td>\n",
+       "      <td>56.636362</td>\n",
+       "      <td>6.086922</td>\n",
+       "      <td>127.924610</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2197</th>\n",
+       "      <td>118</td>\n",
+       "      <td>33</td>\n",
+       "      <td>30</td>\n",
+       "      <td>24.131797</td>\n",
+       "      <td>67.225123</td>\n",
+       "      <td>6.362608</td>\n",
+       "      <td>173.322839</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2198</th>\n",
+       "      <td>117</td>\n",
+       "      <td>32</td>\n",
+       "      <td>34</td>\n",
+       "      <td>26.272418</td>\n",
+       "      <td>52.127394</td>\n",
+       "      <td>6.758793</td>\n",
+       "      <td>127.175293</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2199</th>\n",
+       "      <td>104</td>\n",
+       "      <td>18</td>\n",
+       "      <td>30</td>\n",
+       "      <td>23.603016</td>\n",
+       "      <td>60.396475</td>\n",
+       "      <td>6.779833</td>\n",
+       "      <td>140.937041</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2200 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N   P   K  temperature   humidity        ph    rainfall\n",
+       "0      90  42  43    20.879744  82.002744  6.502985  202.935536\n",
+       "1      85  58  41    21.770462  80.319644  7.038096  226.655537\n",
+       "2      60  55  44    23.004459  82.320763  7.840207  263.964248\n",
+       "3      74  35  40    26.491096  80.158363  6.980401  242.864034\n",
+       "4      78  42  42    20.130175  81.604873  7.628473  262.717340\n",
+       "...   ...  ..  ..          ...        ...       ...         ...\n",
+       "2195  107  34  32    26.774637  66.413269  6.780064  177.774507\n",
+       "2196   99  15  27    27.417112  56.636362  6.086922  127.924610\n",
+       "2197  118  33  30    24.131797  67.225123  6.362608  173.322839\n",
+       "2198  117  32  34    26.272418  52.127394  6.758793  127.175293\n",
+       "2199  104  18  30    23.603016  60.396475  6.779833  140.937041\n",
+       "\n",
+       "[2200 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d2601fcf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "        ..\n",
+       "2195    22\n",
+       "2196    22\n",
+       "2197    22\n",
+       "2198    22\n",
+       "2199    22\n",
+       "Name: crop_num, Length: 2200, dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c561ea31",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2200,)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "caba8efb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "6774a9dd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "41b6bcbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>N</th>\n",
+       "      <th>P</th>\n",
+       "      <th>K</th>\n",
+       "      <th>temperature</th>\n",
+       "      <th>humidity</th>\n",
+       "      <th>ph</th>\n",
+       "      <th>rainfall</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1656</th>\n",
+       "      <td>17</td>\n",
+       "      <td>16</td>\n",
+       "      <td>14</td>\n",
+       "      <td>16.396243</td>\n",
+       "      <td>92.181519</td>\n",
+       "      <td>6.625539</td>\n",
+       "      <td>102.944161</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>752</th>\n",
+       "      <td>37</td>\n",
+       "      <td>79</td>\n",
+       "      <td>19</td>\n",
+       "      <td>27.543848</td>\n",
+       "      <td>69.347863</td>\n",
+       "      <td>7.143943</td>\n",
+       "      <td>69.408782</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>892</th>\n",
+       "      <td>7</td>\n",
+       "      <td>73</td>\n",
+       "      <td>25</td>\n",
+       "      <td>27.521856</td>\n",
+       "      <td>63.132153</td>\n",
+       "      <td>7.288057</td>\n",
+       "      <td>45.208411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1041</th>\n",
+       "      <td>101</td>\n",
+       "      <td>70</td>\n",
+       "      <td>48</td>\n",
+       "      <td>25.360592</td>\n",
+       "      <td>75.031933</td>\n",
+       "      <td>6.012697</td>\n",
+       "      <td>116.553145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1179</th>\n",
+       "      <td>0</td>\n",
+       "      <td>17</td>\n",
+       "      <td>30</td>\n",
+       "      <td>35.474783</td>\n",
+       "      <td>47.972305</td>\n",
+       "      <td>6.279134</td>\n",
+       "      <td>97.790725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1638</th>\n",
+       "      <td>10</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>21.213070</td>\n",
+       "      <td>91.353492</td>\n",
+       "      <td>7.817846</td>\n",
+       "      <td>112.983436</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1095</th>\n",
+       "      <td>108</td>\n",
+       "      <td>94</td>\n",
+       "      <td>47</td>\n",
+       "      <td>27.359116</td>\n",
+       "      <td>84.546250</td>\n",
+       "      <td>6.387431</td>\n",
+       "      <td>90.812505</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1130</th>\n",
+       "      <td>11</td>\n",
+       "      <td>36</td>\n",
+       "      <td>31</td>\n",
+       "      <td>27.920633</td>\n",
+       "      <td>51.779659</td>\n",
+       "      <td>6.475449</td>\n",
+       "      <td>100.258567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>11</td>\n",
+       "      <td>124</td>\n",
+       "      <td>204</td>\n",
+       "      <td>13.429886</td>\n",
+       "      <td>80.066340</td>\n",
+       "      <td>6.361141</td>\n",
+       "      <td>71.400430</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>860</th>\n",
+       "      <td>32</td>\n",
+       "      <td>78</td>\n",
+       "      <td>22</td>\n",
+       "      <td>23.970814</td>\n",
+       "      <td>62.355576</td>\n",
+       "      <td>7.007038</td>\n",
+       "      <td>53.409060</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1760 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        N    P    K  temperature   humidity        ph    rainfall\n",
+       "1656   17   16   14    16.396243  92.181519  6.625539  102.944161\n",
+       "752    37   79   19    27.543848  69.347863  7.143943   69.408782\n",
+       "892     7   73   25    27.521856  63.132153  7.288057   45.208411\n",
+       "1041  101   70   48    25.360592  75.031933  6.012697  116.553145\n",
+       "1179    0   17   30    35.474783  47.972305  6.279134   97.790725\n",
+       "...   ...  ...  ...          ...        ...       ...         ...\n",
+       "1638   10    5    5    21.213070  91.353492  7.817846  112.983436\n",
+       "1095  108   94   47    27.359116  84.546250  6.387431   90.812505\n",
+       "1130   11   36   31    27.920633  51.779659  6.475449  100.258567\n",
+       "1294   11  124  204    13.429886  80.066340  6.361141   71.400430\n",
+       "860    32   78   22    23.970814  62.355576  7.007038   53.409060\n",
+       "\n",
+       "[1760 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ab13cdf8",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Scale the features using MinMaxScaler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f19981a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "ms = MinMaxScaler()\n",
+    "\n",
+    "X_train = ms.fit_transform(X_train)\n",
+    "X_test = ms.transform(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "f3f50c64",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.12142857, 0.07857143, 0.045     , ..., 0.9089898 , 0.48532225,\n",
+       "        0.29685161],\n",
+       "       [0.26428571, 0.52857143, 0.07      , ..., 0.64257946, 0.56594073,\n",
+       "        0.17630752],\n",
+       "       [0.05      , 0.48571429, 0.1       , ..., 0.57005802, 0.58835229,\n",
+       "        0.08931844],\n",
+       "       ...,\n",
+       "       [0.07857143, 0.22142857, 0.13      , ..., 0.43760347, 0.46198144,\n",
+       "        0.28719815],\n",
+       "       [0.07857143, 0.85      , 0.995     , ..., 0.76763665, 0.44420505,\n",
+       "        0.18346657],\n",
+       "       [0.22857143, 0.52142857, 0.085     , ..., 0.56099735, 0.54465022,\n",
+       "        0.11879596]])"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "752a08ae",
+   "metadata": {},
+   "source": [
+    "# Training Models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "ac6ef55e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Support Vector Machine with accuracy: 0.9681818181818181\n",
+      "Confusion matrix: [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 20  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "K-Nearest Neighbors with accuracy: 0.9704545454545455\n",
+      "Confusion matrix: [[14  0  5  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 1  0 22  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1 19  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  3  0  0 21  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 20  2  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n",
+      "Random Forest with accuracy: 0.9931818181818182\n",
+      "Confusion matrix: [[17  0  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0 21  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0 27  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0 17  0  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0 14  0  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0 21  0  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0 11  0  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 19  0  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0 23  0  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 23  0  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 26  0]\n",
+      " [ 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 17]]\n",
+      "==========================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "# create instances of only the selected models\n",
+    "models = {\n",
+    "    'Support Vector Machine': SVC(),\n",
+    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
+    "    'Random Forest': RandomForestClassifier(),\n",
+    "}\n",
+    "\n",
+    "# iterate through the selected models\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    print(f\"{name} with accuracy: {accuracy_score(y_test, y_pred)}\")\n",
+    "    print(\"Confusion matrix:\", confusion_matrix(y_test, y_pred))\n",
+    "    print(\"==========================================================\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "e63aba03-0610-4864-87a1-7f755bdfaf07",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Support Vector Machine with accuracy: 0.9681818181818181\n",
+      "K-Nearest Neighbors with accuracy: 0.9704545454545455\n",
+      "Random Forest with accuracy: 0.9931818181818182\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from sklearn.svm import SVC\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
+    "\n",
+    "# create instances of only the selected models\n",
+    "models = {\n",
+    "    'Support Vector Machine': SVC(),\n",
+    "    'K-Nearest Neighbors': KNeighborsClassifier(),\n",
+    "    'Random Forest': RandomForestClassifier(),\n",
+    "}\n",
+    "\n",
+    "# to store model names and accuracies for plotting\n",
+    "model_names = []\n",
+    "accuracies = []\n",
+    "\n",
+    "# iterate through the selected models\n",
+    "for name, model in models.items():\n",
+    "    model.fit(X_train, y_train)\n",
+    "    y_pred = model.predict(X_test)\n",
+    "    \n",
+    "    accuracy = accuracy_score(y_test, y_pred)\n",
+    "    model_names.append(name)\n",
+    "    accuracies.append(accuracy)\n",
+    "    \n",
+    "    print(f\"{name} with accuracy: {accuracy}\")\n",
+    " \n",
+    "\n",
+    "# set up the DataFrame for Seaborn\n",
+    "import pandas as pd\n",
+    "results_df = pd.DataFrame({'Algorithm': model_names, 'Accuracy': accuracies})\n",
+    "\n",
+    "# plot the accuracies using Seaborn\n",
+    "plt.figure(figsize=(10, 5), dpi=100)\n",
+    "sns.barplot(x='Accuracy', y='Algorithm', data=results_df, palette='dark')\n",
+    "plt.title('Accuracy Comparison')\n",
+    "plt.xlabel('Accuracy')\n",
+    "plt.ylabel('Algorithm')\n",
+    "plt.xlim(0, 1)  # assuming accuracy is between 0 and 1\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "4659be4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9931818181818182"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rfc = RandomForestClassifier()\n",
+    "rfc.fit(X_train,y_train)\n",
+    "ypred = rfc.predict(X_test)\n",
+    "accuracy_score(y_test,ypred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "859d9922",
+   "metadata": {},
+   "source": [
+    "# Predictive System"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "17f3a3fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recommendation(N, P, K, temperature, humidity, ph, rainfall):\n",
+    "    features = np.array([[N, P, K, temperature, humidity, ph, rainfall]])\n",
+    "    transformed_features = ms.transform(features)  # Use transform only\n",
+    "    prediction = rfc.predict(transformed_features)\n",
+    "    return prediction[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "64ffd9d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kidneybeans is a best crop to be cultivated \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs\n",
+    "\n",
+    "\n",
+    "N = 40\n",
+    "P = 50\n",
+    "k = 50\n",
+    "temperature = 40.0\n",
+    "humidity = 20\n",
+    "ph = 100\n",
+    "rainfall = 100\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "2ea8ffda",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Banana is a best crop to be cultivated \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs 2\n",
+    "\n",
+    "\n",
+    "N = 100\n",
+    "P = 90\n",
+    "k = 100\n",
+    "temperature = 50.0\n",
+    "humidity = 90.0\n",
+    "ph = 100\n",
+    "rainfall = 202.0\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d0dccd4e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Orange is a best crop to be cultivated \n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# new inputs 2\n",
+    "N = 10\n",
+    "P = 10\n",
+    "k = 10\n",
+    "temperature = 15.0\n",
+    "humidity = 80.0\n",
+    "ph = 4.5\n",
+    "rainfall = 10.0\n",
+    "\n",
+    "predict = recommendation(N,P,k,temperature,humidity,ph,rainfall)\n",
+    "\n",
+    "crop_dict = {1: \"Rice\", 2: \"Maize\", 3: \"Jute\", 4: \"Cotton\", 5: \"Coconut\", 6: \"Papaya\", 7: \"Orange\",\n",
+    "                 8: \"Apple\", 9: \"Muskmelon\", 10: \"Watermelon\", 11: \"Grapes\", 12: \"Mango\", 13: \"Banana\",\n",
+    "                 14: \"Pomegranate\", 15: \"Lentil\", 16: \"Blackgram\", 17: \"Mungbean\", 18: \"Mothbeans\",\n",
+    "                 19: \"Pigeonpeas\", 20: \"Kidneybeans\", 21: \"Chickpea\", 22: \"Coffee\"}\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is a best crop to be cultivated \".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry are not able to recommend a proper crop for this environment\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "6761fdaf-6bd8-464e-b61a-2cff2f98e08d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Papaya is the best crop to be cultivated.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "E:\\anaconda\\Lib\\site-packages\\sklearn\\base.py:493: UserWarning: X does not have valid feature names, but MinMaxScaler was fitted with feature names\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "N = 40    # Nitrogen\n",
+    "P = 45    # Phosphorus\n",
+    "K = 40    # Potassium\n",
+    "temperature = 20.0  # Celsius\n",
+    "humidity = 80.0     # Percentage\n",
+    "ph = 6.0            # pH\n",
+    "rainfall = 150.0    # mm\n",
+    "\n",
+    "predict = recommendation(N, P, K, temperature, humidity, ph, rainfall)\n",
+    "\n",
+    "if predict in crop_dict:\n",
+    "    crop = crop_dict[predict]\n",
+    "    print(\"{} is the best crop to be cultivated.\".format(crop))\n",
+    "else:\n",
+    "    print(\"Sorry, we are not able to recommend a proper crop for this environment.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "fa3d3b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "pickle.dump(rfc,open('model.pkl','wb'))\n",
+    "pickle.dump(ms,open('minmaxscaler.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a55a48a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c97733fc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Dockerfile

@@ -0,0 +1,17 @@
+# Use Python 3.9 as the base image
+FROM python:3.9
+
+# Set the working directory
+WORKDIR /app
+
+# Copy all project files
+COPY . /app
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Expose the port Flask runs on
+EXPOSE 7860
+
+# Start Flask app using Gunicorn
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]

README.md

@@ -1,10 +1,32 @@
 ---
-title: Agrocrop
-emoji: 📊
-colorFrom: blue
-colorTo: gray
+title: AgroAssist
+emoji: 🌾
+colorFrom: green
+colorTo: yellow
 sdk: docker
 pinned: false
+short_description: A smart crop recommendation system using machine learning
 ---
 
+# AgroAssist - Crop Recommendation System
+
+A smart crop recommendation system that helps farmers choose the best crops based on soil conditions and environmental factors.
+
+## Features
+- AI-powered crop recommendations
+- Soil analysis (NPK values)
+- Climate assessment
+- 22+ crop varieties supported
+- Interactive data visualization
+
+## Technical Stack
+- Flask
+- Scikit-learn
+- NumPy
+- Pandas
+- Bootstrap
+- PowerBI integration
+
+## Directory Structure
+
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,82 @@
+from flask import Flask, render_template, request
+import numpy as np
+import pandas as pd
+import pickle
+import os
+from config import IMAGE_BASE_URL
+from image_utils import get_base64_images
+
+app = Flask(__name__)
+
+# Load images once at startup
+IMAGES = get_base64_images()
+
+# Add this function
+@app.context_processor
+def utility_processor():
+    def get_image_url(path):
+        return IMAGE_BASE_URL + path
+    return dict(get_image_url=get_image_url, images=IMAGES)
+
+# Update model loading with error handling
+try:
+    model = pickle.load(open('model.pkl', 'rb'))
+    sc = pickle.load(open('standscaler.pkl', 'rb'))
+    ms = pickle.load(open('minmaxscaler.pkl', 'rb'))
+except Exception as e:
+    print(f"Error loading models: {e}")
+    # Provide default/dummy models if loading fails
+    from sklearn.ensemble import RandomForestClassifier
+    model = RandomForestClassifier()
+    from sklearn.preprocessing import StandardScaler, MinMaxScaler
+    sc = StandardScaler()
+    ms = MinMaxScaler()
+
+# Define routes
+@app.route('/')
+def home():
+    return render_template('home.html')
+
+@app.route('/info')
+def info():
+    return render_template('info.html')
+
+@app.route('/recommendation', methods=['GET', 'POST'])
+def recommendation():
+    result = None
+    N = P = K = temp = humidity = ph = rainfall = None
+
+    if request.method == 'POST':
+        try:
+            N = float(request.form['Nitrogen'])
+            P = float(request.form['Phosporus'])
+            K = float(request.form['Potassium'])
+            temp = float(request.form['Temperature'])
+            humidity = float(request.form['Humidity'])
+            ph = float(request.form['Ph'])
+            rainfall = float(request.form['Rainfall'])
+
+            feature_list = [N, P, K, temp, humidity, ph, rainfall]
+            single_pred = np.array(feature_list).reshape(1, -1)
+
+            scaled_features = ms.transform(single_pred)
+            final_features = sc.transform(scaled_features)
+            prediction = model.predict(final_features)
+
+            crop_dict = {1: "Rice", 2: "Maize", 3: "Jute", 4: "Cotton", 5: "Coconut", 6: "Papaya", 7: "Orange",
+                        8: "Apple", 9: "Muskmelon", 10: "Watermelon", 11: "Grapes", 12: "Mango", 13: "Banana",
+                        14: "Pomegranate", 15: "Lentil", 16: "Blackgram", 17: "Mungbean", 18: "Mothbeans",
+                        19: "Pigeonpeas", 20: "Kidneybeans", 21: "Chickpea", 22: "Coffee"}
+
+            if prediction[0] in crop_dict:
+                crop = crop_dict[prediction[0]]
+                result = "{} is the best crop to be cultivated right there".format(crop)
+            else:
+                result = "Sorry, we could not determine the best crop to be cultivated with the provided data."
+        except Exception as e:
+            result = f"An error occurred: {str(e)}"
+
+    return render_template('recommendation.html', result=result, N=N, P=P, K=K, temp=temp, humidity=humidity, ph=ph, rainfall=rainfall)
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

config.py

@@ -0,0 +1,2 @@
+# Base URL for images
+IMAGE_BASE_URL = "https://raw.githubusercontent.com/YOUR_USERNAME/YOUR_REPO/main/static/images/" 

image_utils.py

@@ -0,0 +1,22 @@
+import base64
+import os
+
+def get_base64_images():
+    images = {}
+    static_dir = 'static/images'
+    
+    # Logo and banner
+    with open(os.path.join(static_dir, 'logo.png'), 'rb') as f:
+        images['logo'] = base64.b64encode(f.read()).decode()
+    
+    with open(os.path.join(static_dir, 'banner.png'), 'rb') as f:
+        images['banner'] = base64.b64encode(f.read()).decode()
+    
+    # Crop images
+    crops_dir = os.path.join(static_dir, 'crops')
+    for image_file in os.listdir(crops_dir):
+        if image_file.endswith(('.jpg', '.png')):
+            with open(os.path.join(crops_dir, image_file), 'rb') as f:
+                images[image_file.split('.')[0]] = base64.b64encode(f.read()).decode()
+    
+    return images 

minmaxscaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65e53de7d1cfb340ff19db259755347561272c4c4fd9837eee5b19b259f90159
+size 901

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:16a4730e9ab6813c3ff2ba81c61b53d85e38f0aad0823f93a87cd41bd4fa39b7
+size 3641102

requirements.txt

@@ -0,0 +1,6 @@
+flask==2.0.1
+numpy==1.21.0
+pandas==1.3.0
+scikit-learn==0.24.2
+gunicorn==20.1.0
+requests

standscaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:034d859860b10c9e2f3f3fd06cadb7a274488a2fb90be8262c20b5351ebccf4f
+size 617

templates/home.html

@@ -0,0 +1,812 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Crop Recommendation System</title>
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha3/dist/css/bootstrap.min.css" rel="stylesheet">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
+
+    <style>
+        /* Banner styling */
+        .banner {
+            background-color: #ffffff; /* Placeholder background color */
+            height: 85vh; /* 60% of viewport height */
+            width: 1500px;
+            border-radius: 10px; /* Rounded corners */
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            margin: 20px auto; /* Centered with some margin */
+            max-width: 1500px; /* Maximum width for the banner */
+            box-shadow: 0 3px 15px rgba(255, 255, 255, 0.1); /* Optional shadow */
+            position: relative; /* Needed for absolute positioning of button */
+        }
+        .banner img {
+            max-width: 75%; /* Responsive image */n
+            max-height: 75%; /* Prevents overflow */
+            border-radius: 20px; /* Match the banner's rounded corners */
+        }
+        .predict-button {
+            position: absolute; /* Position relative to the banner */
+            left: 35%; /* Align to the left */
+            top: 70; /* Align vertically */
+            transform: translateY(-50%); /* Center the button vertically */
+            background-color: #003400; /* Dark green color */
+            color: rgb(255, 255, 255); /* Button text color */
+            border: none; /* Remove border */
+            text-decoration: none;
+            padding: 12px 35px; /* Button padding */
+            border-radius: 5px; /* Rounded corners */
+            font-size: 20px;
+            font-weight: 700;
+        }
+        .info-section {
+            padding: 20px;
+            background-color: #f8f9fa; /* Light grey background for the info section */
+            border-radius: 10px; /* Rounded corners */
+            margin: 100px auto; /* 100px margin */
+            max-width: 1200px; /* Maximum width for the info section */
+        }
+        .info-section h3 {
+            font-weight: 600; /* Bold heading */
+            margin-bottom: 15px; /* Spacing below the heading */
+        }
+        .info-section h4 {
+            font-weight: 500; /* Medium weight for subheading */
+            margin-top: 15px; /* Spacing above the subheading */
+        }
+        .info-section p {
+            margin-bottom: 15px; /* Spacing below paragraphs */
+
+        }
+        .crop-list {
+            max-height: 400px;
+            overflow-y: auto;
+            padding: 15px;
+        }
+        .crop-item {
+            display: flex;
+            align-items: center;
+            margin-bottom: 15px;
+        }
+        .crop-image {
+            display: block;
+            margin: 0 auto;
+            width: 260px;
+            height: 260px;
+            object-fit: cover;
+            border-radius: 9px;
+        }
+        .nav-item {
+            font-weight: 500;
+        }
+        .navbar {
+            background-color: #0d1025;
+            padding: 0.5rem 1rem;
+            position: sticky;
+            top: 0;
+            z-index: 1000;
+            min-height: 65px;
+        }
+
+        .navbar-brand {
+            display: flex;
+            align-items: center;
+        }
+
+        .logo-img {
+            width: 50px;
+            height: 50px;
+            transition: transform 0.3s;
+        }
+
+        .navbar-toggler {
+            border: 1px solid rgba(255,255,255,0.1);
+            padding: 0.25rem 0.75rem;
+        }
+
+        .navbar-toggler:focus {
+            box-shadow: none;
+            outline: none;
+        }
+
+        .navbar-collapse {
+            flex-grow: 0;
+        }
+
+        .navbar-nav {
+            align-items: center;
+        }
+
+        .nav-link {
+            color: white !important;
+            padding: 0.5rem 1rem !important;
+            margin: 0 0.2rem;
+            border-radius: 5px;
+            transition: all 0.3s ease;
+        }
+
+        .nav-link:hover {
+            background-color: rgba(255, 255, 255, 0.1);
+            transform: translateY(-2px);
+        }
+
+        .brand-text {
+            color: #737373;
+            margin-left: 1rem;
+        }
+
+        /* Fix predict button size */
+        .predict-button {
+            position: absolute;
+            left: 31%;
+            transform: translateX(-50%);
+            bottom: 25%;
+            white-space: nowrap;
+            padding: 12px 35px;
+            font-size: 18px;
+            background-color: #003400;
+            color: white;
+            border: none;
+            border-radius: 5px;
+            transition: all 0.3s ease;
+        }
+
+        .predict-button:hover {
+            transform: translateX(-50%) translateY(-2px);
+            box-shadow: 0 4px 12px rgba(0,0,0,0.2);
+        }
+
+        @media (max-width: 991px) {
+            .navbar-collapse {
+                background-color: #0d1025;
+                padding: 1rem;
+                border-radius: 0 0 10px 10px;
+                position: absolute;
+                top: 100%;
+                left: 0;
+                right: 0;
+                z-index: 1000;
+            }
+
+            .nav-link {
+                text-align: center;
+            }
+
+            .brand-text {
+                margin: 0.5rem 0;
+                text-align: center;
+                display: block;
+            }
+        }
+
+        /* Responsive Banner Styles */
+        .banner {
+            width: 100%;
+            max-width: 1500px;
+            height: auto;
+            min-height: 60vh;
+            margin: 1rem auto;
+            padding: 1rem;
+        }
+
+        /* Responsive Card Grid */
+        .crop-image {
+            width: 100%;
+            height: 200px;
+            object-fit: cover;
+        }
+
+        .card {
+            transition: transform 0.3s ease, box-shadow 0.3s ease;
+            margin-bottom: 1rem;
+        }
+
+        .card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 5px 15px rgba(0,0,0,0.3);
+        }
+
+        /* Media Queries */
+        @media (max-width: 768px) {
+            .banner {
+                height: 50vh;
+            }
+
+            .predict-button {
+                font-size: 16px;
+                padding: 8px 20px;
+            }
+
+            .logo-img {
+                width: 50px;
+                height: 50px;
+            }
+        }
+
+        @media (max-width: 576px) {
+            .banner {
+                height: 40vh;
+            }
+
+            .info-section {
+                padding: 1rem;
+            }
+
+            .crop-image {
+                height: 150px;
+            }
+        }
+
+        /* Card hover effects */
+        .card {
+            border: none;
+            box-shadow: 0 2px 15px rgba(0,0,0,0.1);
+        }
+
+        .card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 5px 20px rgba(0,0,0,0.2);
+        }
+
+        /* Info section improvements */
+        .info-section {
+            background: linear-gradient(145deg, #ffffff, #f8f9fa);
+            box-shadow: 0 4px 15px rgba(0,0,0,0.05);
+        }
+
+        .info-section ul li {
+            margin-bottom: 1rem;
+            padding-left: 1rem;
+            position: relative;
+        }
+
+        .info-section ul li::before {
+            content: "•";
+            color: #003400;
+            font-weight: bold;
+            position: absolute;
+            left: -1rem;
+        }
+
+        /* Crop list improvements */
+        .crop-list {
+            scrollbar-width: thin;
+            scrollbar-color: #003400 #f0f0f0;
+        }
+
+        .crop-list::-webkit-scrollbar {
+            width: 8px;
+        }
+
+        .crop-list::-webkit-scrollbar-track {
+            background: #f0f0f0;
+            border-radius: 10px;
+        }
+
+        .crop-list::-webkit-scrollbar-thumb {
+            background-color: #003400;
+            border-radius: 10px;
+        }
+
+        /* Footer improvements */
+        footer {
+            background: linear-gradient(to right, #0d1025, #1a1a2e);
+            padding: 2rem 0;
+        }
+
+        footer p {
+            margin: 0.5rem 0;
+            opacity: 0.9;
+        }
+
+        .flow-section {
+            padding: 3rem 0;
+        }
+
+        .flow-card {
+            background: white;
+            padding: 2rem;
+            border-radius: 15px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+            text-align: center;
+            transition: all 0.3s ease;
+            height: 100%;
+            position: relative;
+        }
+
+        .flow-card:hover {
+            transform: translateY(-10px);
+            box-shadow: 0 8px 25px rgba(0,0,0,0.2);
+        }
+
+        .flow-card::after {
+            content: "→";
+            position: absolute;
+            right: -25px;
+            top: 50%;
+            transform: translateY(-50%);
+            font-size: 2rem;
+            color: #003400;
+            font-weight: bold;
+        }
+
+        .flow-card:last-child::after {
+            display: none;
+        }
+
+        .flow-icon {
+            width: 80px;
+            height: 80px;
+            background: #003400;
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            margin: 0 auto 1.5rem;
+        }
+
+        .flow-icon i {
+            font-size: 2rem;
+            color: white;
+        }
+
+        .flow-card h4 {
+            color: #003400;
+            margin-bottom: 1rem;
+        }
+
+        .flow-card p {
+            color: #666;
+            font-size: 0.95rem;
+            line-height: 1.5;
+        }
+
+        @media (max-width: 768px) {
+            .flow-card::after {
+                content: "↓";
+                right: 50%;
+                bottom: -25px;
+                top: auto;
+                transform: translateX(50%);
+            }
+
+            .flow-card {
+                margin-bottom: 2rem;
+            }
+        }
+
+        /* Add Features Section */
+        .features-section {
+            padding: 3rem 0;
+        }
+
+        .feature-card {
+            background: white;
+            border-radius: 15px;
+            padding: 2rem;
+            height: 100%;
+            position: relative;
+            overflow: hidden;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.1);
+            transition: all 0.3s ease;
+        }
+
+        .feature-card:hover {
+            transform: translateY(-10px);
+            box-shadow: 0 8px 25px rgba(0,0,0,0.2);
+        }
+
+        .feature-icon {
+            width: 70px;
+            height: 70px;
+            background: linear-gradient(135deg, #003400, #006400);
+            border-radius: 50%;
+            display: flex;
+            align-items: center;
+            justify-content: center;
+            margin-bottom: 1.5rem;
+            transition: all 0.3s ease;
+        }
+
+        .feature-card:hover .feature-icon {
+            transform: scale(1.1) rotate(5deg);
+        }
+
+        .feature-icon i {
+            font-size: 1.8rem;
+            color: white;
+        }
+
+        .feature-content h4 {
+            color: #003400;
+            margin-bottom: 1rem;
+            font-weight: 600;
+        }
+
+        .feature-content p {
+            color: #666;
+            font-size: 0.95rem;
+            line-height: 1.5;
+        }
+
+        .feature-hover {
+            position: absolute;
+            bottom: -40px;
+            left: 0;
+            right: 0;
+            background: linear-gradient(135deg, #003400, #006400);
+            padding: 0.5rem;
+            text-align: center;
+            transition: all 0.3s ease;
+            opacity: 0;
+        }
+
+        .feature-card:hover .feature-hover {
+            bottom: 0;
+            opacity: 1;
+        }
+
+        .hover-text {
+            color: white;
+            font-size: 0.9rem;
+            font-weight: 500;
+        }
+
+        @media (max-width: 768px) {
+            .feature-card {
+                margin-bottom: 2rem;
+            }
+        }
+    </style>
+</head>
+<body>
+    <nav class="navbar navbar-expand-lg navbar-dark">
+        <div class="container-fluid">
+            <a href="/" class="navbar-brand">
+                <img src="data:image/png;base64,{{ images['logo'] }}" alt="AgroAssist Logo" class="logo-img">
+            </a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarSupportedContent">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <div class="collapse navbar-collapse" id="navbarSupportedContent">
+                <ul class="navbar-nav me-auto mb-2 mb-lg-0">
+                    <li class="nav-item">
+                        <a class="nav-link" href="/"><i class="fas fa-house"></i> Home</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" href="/recommendation"><i class="fas fa-seedling"></i> Recommend</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" href="/info"><i class="fas fa-chart-bar"></i> Statistics</a>
+                    </li>
+                </ul>
+                <span class="navbar-text brand-text">AgroAssist</span>
+            </div>
+        </div>
+    </nav>
+    
+
+    <!-- Banner Section -->
+    <div class="banner">
+        <img src="data:image/png;base64,{{ images['banner'] }}" alt="Banner Image">
+        <a href="/recommendation" class="predict-button">Click to Predict →</a> <!-- Button added here -->
+    </div>
+
+    <!-- Information Section -->
+
+
+    <!-- Add this after the info-section div -->
+    <div class="container flow-section my-5">
+        <h3 class="text-center mb-5">How AgroAssist Works</h3>
+        <div class="row">
+            <div class="col-md-4 mb-4">
+                <div class="flow-card">
+                    <div class="flow-icon">
+                        <i class="fas fa-flask"></i>
+                    </div>
+                    <h4>1. Soil Analysis</h4>
+                    <p>Input your soil's NPK values and we'll analyze the nutrient content to determine soil health.</p>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="flow-card">
+                    <div class="flow-icon">
+                        <i class="fas fa-cloud-sun"></i>
+                    </div>
+                    <h4>2. Climate Assessment</h4>
+                    <p>We evaluate temperature, humidity, and rainfall patterns to assess environmental conditions.</p>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="flow-card">
+                    <div class="flow-icon">
+                        <i class="fas fa-leaf"></i>
+                    </div>
+                    <h4>3. Smart Recommendation</h4>
+                    <p>Our AI model processes the data to suggest the most suitable crops for your farm.</p>
+                </div>
+            </div>
+        </div>
+    </div>
+
+    <!-- Add Features Section -->
+    <div class="container features-section my-5">
+        <h3 class="text-center mb-5">Key Features</h3>
+        <div class="row">
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-robot"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>AI-Powered Analysis</h4>
+                        <p>Advanced machine learning algorithms analyze your farm's conditions to provide accurate crop recommendations.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">Uses Random Forest Algorithm</span>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-chart-line"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>Data Visualization</h4>
+                        <p>Interactive charts and graphs help you understand crop requirements and environmental conditions.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">PowerBI Integration</span>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-mobile-alt"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>Mobile Responsive</h4>
+                        <p>Access the system from any device - desktop, tablet, or mobile phone with full functionality.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">Cross-Platform Compatible</span>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-database"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>Comprehensive Database</h4>
+                        <p>Extensive database of crops with their optimal growing conditions and requirements.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">20+ Crop Varieties</span>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-cloud-sun-rain"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>Climate Analysis</h4>
+                        <p>Considers temperature, humidity, and rainfall patterns for optimal crop selection.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">Real-time Weather Data</span>
+                    </div>
+                </div>
+            </div>
+            <div class="col-md-4 mb-4">
+                <div class="feature-card">
+                    <div class="feature-icon">
+                        <i class="fas fa-leaf"></i>
+                    </div>
+                    <div class="feature-content">
+                        <h4>Soil Health Analysis</h4>
+                        <p>Evaluates soil nutrients (NPK) and pH levels to ensure optimal growing conditions.</p>
+                    </div>
+                    <div class="feature-hover">
+                        <span class="hover-text">Detailed Soil Reports</span>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+
+   <!-- Crop Scrolling List -->
+<h3 class="text-center mt-5">Our Crops</h3>
+<div class="container">
+    <div class="row justify-content-center">
+        <div class="col-md-8 crop-list mx-auto">
+            <div class="row">
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['rice'] }}" class="card-img-top crop-image" alt="Rice">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Rice</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['cotton'] }}" class="card-img-top crop-image" alt="Cotton">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Cotton</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['jute'] }}" class="card-img-top crop-image" alt="Jute">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Jute</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['papaya'] }}" class="card-img-top crop-image" alt="Papaya">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Papaya</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['maize'] }}" class="card-img-top crop-image" alt="Maize">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Maize</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['moth'] }}" class="card-img-top crop-image" alt="Moth Bean">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Moth Bean</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['black'] }}" class="card-img-top crop-image" alt="Black Gram">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Black Gram</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['kidney'] }}" class="card-img-top crop-image" alt="Kidney Beans">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Kidney Beans</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['peas'] }}" class="card-img-top crop-image" alt="Pigeon Peas">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Pigeon Peas</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['pomo'] }}" class="card-img-top crop-image" alt="Pomegranate">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Pomegranate</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['coffe'] }}" class="card-img-top crop-image" alt="Coffee">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Coffee</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['muskmelon'] }}" class="card-img-top crop-image" alt="Muskmelon">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Muskmelon</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['watermelon'] }}" class="card-img-top crop-image" alt="Watermelon">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Watermelon</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['chik'] }}" class="card-img-top crop-image" alt="Chickpea">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Chickpea</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['bananan'] }}" class="card-img-top crop-image" alt="Banana">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Banana</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['apple'] }}" class="card-img-top crop-image" alt="Apple">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Apple</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['grapes'] }}" class="card-img-top crop-image" alt="Grapes">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Grapes</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['orange'] }}" class="card-img-top crop-image" alt="Orange">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Orange</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['coconut'] }}" class="card-img-top crop-image" alt="Coconut">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Coconut</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['mango'] }}" class="card-img-top crop-image" alt="Mango">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Mango</h5>
+                        </div>
+                    </div>
+                </div>
+                <div class="col-6 col-md-4 mb-4">
+                    <div class="card">
+                        <img src="data:image/jpeg;base64,{{ images['lent'] }}" class="card-img-top crop-image" alt="Lentil">
+                        <div class="card-body text-center">
+                            <h5 class="card-title">Lentil</h5>
+                        </div>
+                    </div>
+                </div>
+            </div>
+        </div>
+    </div>
+</div>
+
+<!-- Optional Footer -->
+<footer class="bg-dark text-white text-center py-3 mt-5">
+    <p>&copy; 2024-25 AgroAssist. All rights reserved.</p>
+    <p> created by Gunjankumar Choudhari</p>
+</footer>
+
+<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0-alpha3/dist/js/bootstrap.bundle.min.js" integrity="sha384-ENjdO4Dr2bkBIFxQpeoTz1HIcje39Wm4jDKdf19U8gI4ddQ3GYNS7NTKfAdVQSZe" crossorigin="anonymous"></script>
+</body>
+</html>

templates/info.html

@@ -0,0 +1,218 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Crops Info Dashboard</title>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap/5.1.3/css/bootstrap.min.css"> 
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
+    <link href="https://fonts.googleapis.com/css2?family=Poppins:wght@700&display=swap" rel="stylesheet">
+    <style>
+        .custom-font {
+            font-family: 'Poppins', sans-serif;
+            font-size: 2.5rem;
+            color: #003400;
+            margin: 1rem 0;
+        }
+
+        body {
+            background-color: #f4f4f9;
+        }
+
+        /* Navbar Styles */
+        .navbar {
+            background-color: #0d1025;
+            padding: 0.5rem 1rem;
+            position: sticky;
+            top: 0;
+            z-index: 1000;
+            min-height: 65px;
+        }
+
+        .navbar-brand {
+            display: flex;
+            align-items: center;
+        }
+
+        .logo-img {
+            width: 50px;
+            height: 50px;
+            transition: transform 0.3s;
+        }
+
+        .navbar-toggler {
+            border: 1px solid rgba(255,255,255,0.1);
+            padding: 0.25rem 0.75rem;
+        }
+
+        .navbar-toggler:focus {
+            box-shadow: none;
+            outline: none;
+        }
+
+        .navbar-collapse {
+            flex-grow: 0;
+        }
+
+        .navbar-nav {
+            align-items: center;
+        }
+
+        .nav-link {
+            color: white !important;
+            padding: 0.5rem 1rem !important;
+            margin: 0 0.2rem;
+            border-radius: 5px;
+            transition: all 0.3s ease;
+        }
+
+        .nav-link:hover {
+            background-color: rgba(255, 255, 255, 0.1);
+            transform: translateY(-2px);
+        }
+
+        .brand-text {
+            color: #737373;
+            margin-left: 1rem;
+        }
+
+        @media (max-width: 991px) {
+            .navbar-collapse {
+                background-color: #0d1025;
+                padding: 1rem;
+                border-radius: 0 0 10px 10px;
+                position: absolute;
+                top: 100%;
+                left: 0;
+                right: 0;
+                z-index: 1000;
+            }
+
+            .nav-link {
+                text-align: center;
+            }
+
+            .brand-text {
+                margin: 0.5rem 0;
+                text-align: center;
+                display: block;
+            }
+        }
+
+        /* Dashboard Card Styles */
+        .dashboard-card {
+            background: white;
+            border-radius: 10px;
+            box-shadow: 0 4px 8px rgba(0,0,0,0.1);
+            margin-bottom: 2rem;
+            transition: all 0.3s ease;
+        }
+
+        .dashboard-card:hover {
+            transform: translateY(-5px);
+            box-shadow: 0 6px 12px rgba(0,0,0,0.15);
+        }
+
+        /* Chart Description Styles */
+        .chart-desc {
+            padding: 2rem;
+            background: linear-gradient(145deg, #ffffff, #f8f9fa);
+            border-radius: 15px;
+            box-shadow: 0 4px 15px rgba(0,0,0,0.05);
+        }
+
+        .chart-desc h3 {
+            color: #003400;
+            margin-bottom: 1.5rem;
+        }
+
+        .chart-desc p {
+            margin-bottom: 1rem;
+            padding-left: 1.5rem;
+            position: relative;
+        }
+
+        .chart-desc i {
+            color: #003400;
+            margin-right: 10px;
+        }
+
+        /* Iframe Styles */
+        iframe {
+            width: 100%;
+            min-height: 600px;
+            border-radius: 15px;
+            box-shadow: 0 4px 20px rgba(0,0,0,0.1);
+            border: none;
+        }
+
+        /* Footer Styles */
+        footer {
+            background: linear-gradient(to right, #0d1025, #1a1a2e);
+            padding: 2rem 0;
+            margin-top: 3rem;
+        }
+
+        footer p {
+            margin: 0.5rem 0;
+            opacity: 0.9;
+        }
+    </style>
+</head>
+<body>
+
+    <!-- Navbar -->
+    <nav class="navbar navbar-expand-lg navbar-dark">
+        <div class="container-fluid">
+            <a href="/" class="navbar-brand">
+                <img src="data:image/png;base64,{{ images['logo'] }}" alt="AgroAssist Logo" class="logo-img">
+            </a>
+            <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbarSupportedContent">
+                <span class="navbar-toggler-icon"></span>
+            </button>
+            <div class="collapse navbar-collapse" id="navbarSupportedContent">
+                <ul class="navbar-nav me-auto mb-2 mb-lg-0">
+                    <li class="nav-item">
+                        <a class="nav-link" href="/"><i class="fas fa-house"></i> Home</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" href="/recommendation"><i class="fas fa-seedling"></i> Recommend</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" href="/info"><i class="fas fa-chart-bar"></i> Statistics</a>
+                    </li>
+                </ul>
+                <span class="navbar-text brand-text">AgroAssist</span>
+            </div>
+        </div>
+    </nav>
+
+    <!-- Update the chart description section -->
+    <div class="container mt-4">
+        <div class="chart-desc">
+            <h3 class="text-center">Charts Description</h3>
+            <p><i class="fas fa-chart-bar"></i><strong>Nitrogen, Potassium, and Phosphorus of Crops:</strong> This chart shows the nutrient levels of various crops.</p>
+            <p><i class="fas fa-thermometer-half"></i><strong>Temperature and Humidity of Crops:</strong> This plot visualizes the optimal temperature and humidity for different crops.</p>
+            <p><i class="fas fa-flask"></i><strong>Average pH by Crop Label:</strong> This chart displays the average pH values for crops, helping with soil suitability.</p>
+        </div>
+    </div>
+    <br>
+
+    <!-- Power BI Embed Section -->
+    <div class="container mt-5">
+        <h1 class="text-center custom-font">Crops Information Dashboard</h1> <!-- Apply custom class -->
+        <iframe title="crop22_powerbi" width="100%" height="600px" src="https://app.powerbi.com/view?r=eyJrIjoiYzc4YTM2NTMtZjZmNi00NDAzLTg0MzktZTY3YjQ0NzVkNTYzIiwidCI6IjBkMDRhNmQxLWFlNDctNGZjZS04MTgxLWMxYTQwNzI5MTNiYiJ9" frameborder="0" allowfullscreen="true"></iframe>
+    </div>
+
+    <!-- Optional Footer -->
+    <footer class="bg-dark text-white text-center py-3 mt-5">
+        <p>&copy; 2024-25 AgroAssist. All rights reserved.</p>
+        <p> created by Gunjankumar Choudhari</p>
+    </footer>
+
+    <!-- Bootstrap JS -->
+    <!-- Add these before closing body tag in all templates -->
+<script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
+<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.min.js"></script>
+</body>
+</html>

templates/recommendation.html

@@ -0,0 +1,239 @@
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Crop Recommendation</title>
+    <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/css/bootstrap.min.css" rel="stylesheet">
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0-beta3/css/all.min.css">
+
+    <style>
+        .navbar {
+            background-color: #0d1025;
+            padding: 0.5rem 1rem;
+            position: sticky;
+            top: 0;
+            z-index: 1000;
+            min-height: 65px;
+        }
+
+        .navbar-brand {
+            display: flex;
+            align-items: center;
+        }
+
+        .logo-img {
+            width: 50px;
+            height: 50px;
+            transition: transform 0.3s;
+        }
+
+        .navbar-toggler {
+            border: 1px solid rgba(255,255,255,0.1);
+            padding: 0.25rem 0.75rem;
+        }
+
+        .navbar-toggler:focus {
+            box-shadow: none;
+            outline: none;
+        }
+
+        .navbar-collapse {
+            flex-grow: 0;
+        }
+
+        .navbar-nav {
+            align-items: center;
+        }
+
+        .nav-link {
+            color: white !important;
+            padding: 0.5rem 1rem !important;
+            margin: 0 0.2rem;
+            border-radius: 5px;
+            transition: all 0.3s ease;
+        }
+
+        .nav-link:hover {
+            background-color: rgba(255, 255, 255, 0.1);
+            transform: translateY(-2px);
+        }
+
+        .brand-text {
+            color: #737373;
+            margin-left: 1rem;
+        }
+
+        @media (max-width: 991px) {
+            .navbar-collapse {
+                background-color: #0d1025;
+                padding: 1rem;
+                border-radius: 0 0 10px 10px;
+                position: absolute;
+                top: 100%;
+                left: 0;
+                right: 0;
+                z-index: 1000;
+            }
+
+            .nav-link {
+                text-align: center;
+            }
+
+            .brand-text {
+                margin: 0.5rem 0;
+                text-align: center;
+                display: block;
+            }
+        }
+
+        /* Responsive Form Styles */
+        .form-control {
+            margin-bottom: 1rem;
+        }
+
+        .btn {
+            margin: 0.5rem;
+            padding: 0.5rem 2rem;
+            transition: all 0.3s ease;
+        }
+
+        .btn:hover {
+            transform: translateY(-2px);
+            box-shadow: 0 2px 8px rgba(0,0,0,0.2);
+        }
+
+        /* Media Queries */
+        @media (max-width: 768px) {
+            .container {
+                padding: 1rem;
+            }
+
+            h1 {
+                font-size: 1.8rem;
+            }
+        }
+
+        @media (max-width: 576px) {
+            .btn {
+                width: 100%;
+                margin: 0.5rem 0;
+            }
+        }
+    </style>
+</head>
+
+<body>
+    <nav class="navbar navbar-expand-lg navbar-dark" style="background-color: #0d1025; height: 65px;">
+        <div class="container-fluid">
+            <a href="/" class="navbar-brand">
+                <img src="data:image/png;base64,{{ images['logo'] }}" alt="AgroAssist Logo" class="me-2" style="width: 80px; height: 80px;">
+            </a>
+            
+            <div class="collapse navbar-collapse" id="navbarSupportedContent">
+                <ul class="navbar-nav me-auto mb-2 mb-lg-0">
+                    <li class="nav-item">
+                        <a class="nav-link active" aria-current="page" href="/"><i class="fa-solid fa-house"></i>Home</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" style="color: white;" href="/recommendation"><i class="fas fa-seedling"></i>Recommend</a>
+                    </li>
+                    <li class="nav-item">
+                        <a class="nav-link" style="color: white;" href="/info"><i class="fas fa-chart-bar"></i>Statistics</a>
+                    </li>
+                </ul>
+                <a class="navbar-brand ms-auto" style="color: #737373;">AgroAssist</a>
+            </div>
+        </div>
+    </nav>
+    
+    
+
+    <div class="container mt-5">
+        <h1 class="text-center">Crop Recommendation System</h1>
+        <form id="cropRecommendationForm" action="/recommendation" method="POST">
+            <div class="row mt-4">
+                <div class="col-md-4">
+                    <label for="Nitrogen">Nitrogen</label>
+                    <input type="number" step="0.01" id="Nitrogen" name="Nitrogen" placeholder="Enter Nitrogen" class="form-control" required value="{{ N }}">
+                </div>
+                <div class="col-md-4">
+                    <label for="Phosporus">Phosphorus</label>
+                    <input type="number" step="0.01" id="Phosporus" name="Phosporus" placeholder="Enter Phosphorus" class="form-control" required value="{{ P }}">
+                </div>
+                <div class="col-md-4">
+                    <label for="Potassium">Potassium</label>
+                    <input type="number" step="0.01" id="Potassium" name="Potassium" placeholder="Enter Potassium" class="form-control" required value="{{ K }}">
+                </div>
+            </div>
+            <div class="row mt-4">
+                <div class="col-md-4">
+                    <label for="Temperature">Temperature (°C)</label>
+                    <input type="number" step="0.01" id="Temperature" name="Temperature" placeholder="Enter Temperature" class="form-control" required value="{{ temp }}">
+                </div>
+                <div class="col-md-4">
+                    <label for="Humidity">Humidity (%)</label>
+                    <input type="number" step="0.01" id="Humidity" name="Humidity" placeholder="Enter Humidity" class="form-control" required value="{{ humidity }}">
+                </div>
+                <div class="col-md-4">
+                    <label for="Ph">pH</label>
+                    <input type="number" step="0.01" id="Ph" name="Ph" placeholder="Enter pH" class="form-control" required value="{{ ph }}">
+                </div>
+            </div>
+            <div class="row mt-4">
+                <div class="col-md-4">
+                    <label for="Rainfall">Rainfall (mm/year)</label>
+                    <input type="number" step="0.01" id="Rainfall" name="Rainfall" placeholder="Enter Rainfall" class="form-control" required value="{{ rainfall }}">
+                </div>
+                <div class="col-md-12 mt-4 text-center">
+                    <button type="submit" class="btn btn-success">Get Recommendation</button>
+                    <button type="button" class="btn btn-danger" onclick="resetForm()">Reset</button>
+                </div>
+            </div>
+        </form>
+
+        {% if result %}
+            <div class="mt-4 alert alert-success">
+                <h4>{{ result }}</h4>
+            </div>
+        {% endif %}
+
+        <div class="alert alert-info mt-4">
+            <h5>Input Parameter Information</h5>
+            <ul>
+                <li>Nitrogen: 0 - 150 kg/ha</li>
+                <li>Phosphorus: 0 - 150 kg/ha</li>
+                <li>Potassium: 0 - 150 kg/ha</li>
+                <li>Temperature: 0 - 45 °C</li>
+                <li>Humidity: 0 - 100%</li>
+                <li>pH: 0 - 14</li>
+                <li>Rainfall: 100 - 3000 mm/year</li>
+            </ul>
+        </div>
+    </div>
+
+    <footer class="bg-dark text-white text-center py-3 mt-5">
+        <p>&copy; 2024-25 AgroAssist. All rights reserved.</p>
+        <p> created by Gunjankumar Choudhari</p>
+    </footer>
+
+    <script src="https://code.jquery.com/jquery-3.5.1.slim.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/@popperjs/core@2.11.6/dist/umd/popper.min.js"></script>
+    <script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.min.js"></script>
+    <script>
+        function resetForm() {
+            // Clear all input fields
+            document.getElementById('cropRecommendationForm').reset();
+            // Clear the result display
+            const resultElement = document.querySelector('.mt-4.alert-success');
+            if (resultElement) {
+                resultElement.remove();
+            }
+            // Optional user feedback
+            alert("Form has been reset.");
+        }
+    </script>
+</body>
+
+</html>