{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Weather Prediction Model</h1>\n",
    "<h5>Created by: Cristopher Ian Artacho</h5>\n",
    "<h5>BSCS 3A</h5>\n",
    "\n",
    "<h5>Using a dataset from kaggle, the aim of this project is to train a model that could classify the weather into \"snow\", \"drizzle\", \"rain\", \"sunny\", and \"fog\" based on the user input as parameters</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import classification_report, f1_score, accuracy_score\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "import joblib\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Data Exploration and Preprocessing</h1>\n",
    "<h5>In this process, we will get to understand our data, and the dataset. In case that there are missing values, noise, and/or errors in the data, we will need to clean it in order to reduce the complexity of the data, allowing the model to better understand the dataset. </h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "df = pd.read_csv(\"seattle-weather.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1461 entries, 0 to 1460\n",
      "Data columns (total 6 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   date           1461 non-null   object \n",
      " 1   precipitation  1461 non-null   float64\n",
      " 2   temp_max       1461 non-null   float64\n",
      " 3   temp_min       1461 non-null   float64\n",
      " 4   wind           1461 non-null   float64\n",
      " 5   weather        1461 non-null   object \n",
      "dtypes: float64(4), object(2)\n",
      "memory usage: 68.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['date'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We do not need the \"date\" column, because we are mainly focusing on classifying the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac=1, random_state=42).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "weather\n",
      "rain       641\n",
      "sun        640\n",
      "fog        101\n",
      "drizzle     53\n",
      "snow        26\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(df[\"weather\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Feature Engineering</h1>\n",
    "<h5>In this process, we will transform the target variable into numerical, in order for the model to understand the different classes in the column</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_mapping = {\"rain\": 0, \"sun\": 1, \"fog\": 2, \"drizzle\": 3, \"snow\": 4}\n",
    "\n",
    "df[\"weather\"] = df[\"weather\"].map(weather_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
    "plt.title(\"Correlation Matrix\")\n",
    "plt.savefig(\"correlation_matrix.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Training the Model</h1>\n",
    "<h5>After mapping the target variable, it is time to train the model in order to help it classify which of the classifications does the weather parameters belong to.For this model, we will use the Decision Trees</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['weather'])\n",
    "y = df['weather']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;DecisionTreeClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=42)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DTCmodel = DecisionTreeClassifier(random_state=42)\n",
    "DTCmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-11 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-11 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-11 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-11 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-11 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-11 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-11 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-11 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-11 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-11 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-11 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=42)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFCmodel = RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "RFCmodel.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standardize the feature values (important for KNN)\n",
    "scaler = StandardScaler()\n",
    "KNNX_train = scaler.fit_transform(X_train)\n",
    "KNNX_test = scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-12 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-12 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-12 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-12 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-12 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-12 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-12 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-12 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-12 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-12 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNNmodel = KNeighborsClassifier(n_neighbors=5)\n",
    "KNNmodel.fit(KNNX_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predictions\n",
    "DTCy_pred = DTCmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "RFCy_pred = RFCmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cristopher Artacho\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but KNeighborsClassifier was fitted without feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "KNNy_pred = KNNmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Model Evaluation</h1>\n",
    "<h5>After training the model, we will use Evaluation metrics in order to judge if the model's predictions are correct.</h5>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7986\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       139\n",
      "           1       0.84      0.78      0.81       125\n",
      "           2       0.12      0.21      0.15        14\n",
      "           3       0.33      0.17      0.22        12\n",
      "           4       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.80       293\n",
      "   macro avg       0.44      0.42      0.42       293\n",
      "weighted avg       0.81      0.80      0.80       293\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DTCaccuracy = accuracy_score(y_test, DTCy_pred)\n",
    "DTCf1 = f1_score(y_test, DTCy_pred, average=\"weighted\")\n",
    "DTCreport = classification_report(y_test, DTCy_pred)\n",
    "\n",
    "print(f\"Accuracy: {DTCaccuracy:.4f}\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, DTCy_pred))\n",
    "\n",
    "text_output = f\"Accuracy: {DTCaccuracy:.4f}\\nF1 Score: {DTCf1:.4f}\\n\\nClassification Report:\\n{DTCreport}\"\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.text(0.01, 0.99, text_output, fontsize=12, ha='left', va='top', family=\"monospace\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.savefig(\"DTCclassification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reverse_mapping = {v: k for k, v in weather_mapping.items()}\n",
    "\n",
    "y_test_labels = [reverse_mapping[val] for val in y_test]\n",
    "y_pred_labels = [reverse_mapping[val] for val in DTCy_pred]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x=range(len(y_test)), y=y_test_labels, label='Actual', color='blue')\n",
    "sns.scatterplot(x=range(len(DTCy_pred)), y=y_pred_labels, label='Predicted', color='red')\n",
    "plt.xticks([])\n",
    "plt.xlabel(\"Samples\")\n",
    "plt.ylabel(\"Weather Condition\")\n",
    "plt.title(\"Decision Tree Classifier Actual vs Predicted Weather Conditions\")\n",
    "plt.legend()\n",
    "plt.savefig(\"DTCweather_predictions.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully!\n"
     ]
    }
   ],
   "source": [
    "#SAVING THE MODEL AS A .pkl FILE\n",
    "joblib.dump(DTCmodel, \"DTCweather_model.pkl\")\n",
    "print(\"Model saved successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8669\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       139\n",
      "           1       0.84      0.78      0.81       125\n",
      "           2       0.12      0.21      0.15        14\n",
      "           3       0.33      0.17      0.22        12\n",
      "           4       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.80       293\n",
      "   macro avg       0.44      0.42      0.42       293\n",
      "weighted avg       0.81      0.80      0.80       293\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RFCaccuracy = accuracy_score(y_test, RFCy_pred)\n",
    "RFCf1 = f1_score(y_test, RFCy_pred, average=\"weighted\")\n",
    "RFCreport = classification_report(y_test, RFCy_pred)\n",
    "\n",
    "print(f\"Accuracy: {RFCaccuracy:.4f}\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, DTCy_pred))\n",
    "\n",
    "text_output = f\"Accuracy: {RFCaccuracy:.4f}\\nF1 Score: {RFCf1:.4f}\\n\\nClassification Report:\\n{RFCreport}\"\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.text(0.01, 0.99, text_output, fontsize=12, ha='left', va='top', family=\"monospace\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.savefig(\"RFCclassification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reverse_mapping = {v: k for k, v in weather_mapping.items()}\n",
    "\n",
    "y_test_labels = [reverse_mapping[val] for val in y_test]\n",
    "y_pred_labels = [reverse_mapping[val] for val in RFCy_pred]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x=range(len(y_test)), y=y_test_labels, label='Actual', color='blue')\n",
    "sns.scatterplot(x=range(len(DTCy_pred)), y=y_pred_labels, label='Predicted', color='red')\n",
    "plt.xticks([])\n",
    "plt.xlabel(\"Samples\")\n",
    "plt.ylabel(\"Weather Condition\")\n",
    "plt.title(\"Random Forest Classifier Actual vs Predicted Weather Conditions\")\n",
    "plt.legend()\n",
    "plt.savefig(\"RFCweather_predictions.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully!\n"
     ]
    }
   ],
   "source": [
    "#SAVING THE MODEL AS A .pkl FILE\n",
    "joblib.dump(RFCmodel, \"RFCweather_model.pkl\")\n",
    "print(\"Model saved successfully!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6007\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.95      0.93       139\n",
      "           1       0.84      0.78      0.81       125\n",
      "           2       0.12      0.21      0.15        14\n",
      "           3       0.33      0.17      0.22        12\n",
      "           4       0.00      0.00      0.00         3\n",
      "\n",
      "    accuracy                           0.80       293\n",
      "   macro avg       0.44      0.42      0.42       293\n",
      "weighted avg       0.81      0.80      0.80       293\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Cristopher Artacho\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\Cristopher Artacho\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
      "C:\\Users\\Cristopher Artacho\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1517: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "KNNaccuracy = accuracy_score(y_test, KNNy_pred)\n",
    "KNNf1 = f1_score(y_test, KNNy_pred, average=\"weighted\")\n",
    "KNNreport = classification_report(y_test, KNNy_pred)\n",
    "\n",
    "print(f\"Accuracy: {KNNaccuracy:.4f}\")\n",
    "print(\"Classification Report:\\n\", classification_report(y_test, DTCy_pred))\n",
    "\n",
    "text_output = f\"Accuracy: {KNNaccuracy:.4f}\\nF1 Score: {KNNf1:.4f}\\n\\nClassification Report:\\n{KNNreport}\"\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.text(0.01, 0.99, text_output, fontsize=12, ha='left', va='top', family=\"monospace\")\n",
    "plt.axis(\"off\")\n",
    "\n",
    "plt.savefig(\"KNNclassification_report.png\", bbox_inches=\"tight\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reverse_mapping = {v: k for k, v in weather_mapping.items()}\n",
    "\n",
    "y_test_labels = [reverse_mapping[val] for val in y_test]\n",
    "y_pred_labels = [reverse_mapping[val] for val in KNNy_pred]\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(x=range(len(y_test)), y=y_test_labels, label='Actual', color='blue')\n",
    "sns.scatterplot(x=range(len(DTCy_pred)), y=y_pred_labels, label='Predicted', color='red')\n",
    "plt.xticks([])\n",
    "plt.xlabel(\"Samples\")\n",
    "plt.ylabel(\"Weather Condition\")\n",
    "plt.title(\"K Nearest Neighbor Actual vs Predicted Weather Conditions\")\n",
    "plt.legend()\n",
    "plt.savefig(\"KNNweather_predictions.png\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model saved successfully!\n"
     ]
    }
   ],
   "source": [
    "#SAVING THE MODEL AS A .pkl FILE\n",
    "joblib.dump(KNNmodel, \"KNNweather_model.pkl\")\n",
    "print(\"Model saved successfully!\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": 2
}