{
 "cells": [
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   "source": [
    "\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this notebook, we tackle a **DNA sequence classification** task: predicting the **type of gene** (e.g., *PSEUDO*, *BIOLOGICAL\\_REGION*) based on its **nucleotide sequence**.\n",
    "\n",
    "The dataset contains gene entries with their NCBI ID, symbol, description, gene type (our target label), and raw DNA sequences. Our main objective is to explore whether machine learning models can learn meaningful patterns in the sequences to classify gene types accurately.\n",
    "\n",
    "We’ll start by preprocessing the sequences using **k-mer encoding**, then train and evaluate classification models such as Random Forests. This project demonstrates a practical application of machine learning in bioinformatics — bridging the gap between raw DNA data and functional gene annotation.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46468e3b",
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     "start_time": "2025-07-19T12:48:10.221166",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "#### Tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7a65916",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from scipy.sparse import hstack\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMClassifier\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8846ce4",
   "metadata": {
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     "exception": false,
     "start_time": "2025-07-19T12:48:20.696211",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8973917",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:20.718190Z",
     "iopub.status.busy": "2025-07-19T12:48:20.717432Z",
     "iopub.status.idle": "2025-07-19T12:48:21.106112Z",
     "shell.execute_reply": "2025-07-19T12:48:21.105181Z"
    },
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     "duration": 0.398343,
     "end_time": "2025-07-19T12:48:21.107915",
     "exception": false,
     "start_time": "2025-07-19T12:48:20.709572",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/kaggle/input/dna-sequence-prediction/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8a466c50",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:21.123374Z",
     "iopub.status.busy": "2025-07-19T12:48:21.123049Z",
     "iopub.status.idle": "2025-07-19T12:48:21.140162Z",
     "shell.execute_reply": "2025-07-19T12:48:21.139225Z"
    },
    "papermill": {
     "duration": 0.026712,
     "end_time": "2025-07-19T12:48:21.141932",
     "exception": false,
     "start_time": "2025-07-19T12:48:21.115220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.drop(columns=['Unnamed: 0'], errors='ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f21b55ce",
   "metadata": {
    "papermill": {
     "duration": 0.006915,
     "end_time": "2025-07-19T12:48:21.156247",
     "exception": false,
     "start_time": "2025-07-19T12:48:21.149332",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Filter out the unnecessary chars from NucleotideSequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "835c4f22",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:21.171994Z",
     "iopub.status.busy": "2025-07-19T12:48:21.171651Z",
     "iopub.status.idle": "2025-07-19T12:48:21.285344Z",
     "shell.execute_reply": "2025-07-19T12:48:21.284554Z"
    },
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     "duration": 0.123458,
     "end_time": "2025-07-19T12:48:21.286945",
     "exception": false,
     "start_time": "2025-07-19T12:48:21.163487",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['NucleotideSequence'] = df['NucleotideSequence'].str.replace(r'[<>]', '', regex=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5af9fd01",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "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>NCBIGeneID</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Description</th>\n",
       "      <th>GeneType</th>\n",
       "      <th>GeneGroupMethod</th>\n",
       "      <th>NucleotideSequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106481178</td>\n",
       "      <td>RNU4-21P</td>\n",
       "      <td>RNA, U4 small nuclear 21, pseudogene</td>\n",
       "      <td>PSEUDO</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>AGCTTAGCACAGTGGCAGTATCATAGGCAGTGAGGTTTATCCGAGG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>123477792</td>\n",
       "      <td>LOC123477792</td>\n",
       "      <td>Sharpr-MPRA regulatory region 12926</td>\n",
       "      <td>BIOLOGICAL_REGION</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>CTGGAGCGGCCACGATGTGAACTGTCACCGGCCACTGCTGCTCCGA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>113174975</td>\n",
       "      <td>LOC113174975</td>\n",
       "      <td>Sharpr-MPRA regulatory region 7591</td>\n",
       "      <td>BIOLOGICAL_REGION</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>TTCCCAATTTTTCCTCTGCTTTTTAATTTTCTAGTTTCCTTTTTCC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>116216107</td>\n",
       "      <td>LOC116216107</td>\n",
       "      <td>CRISPRi-validated cis-regulatory element chr10...</td>\n",
       "      <td>BIOLOGICAL_REGION</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>CGCCCAGGCTGGAGTGCAGTGGCGCCATCTCGGCTCACTGCAGGCT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28502</td>\n",
       "      <td>IGHD2-21</td>\n",
       "      <td>immunoglobulin heavy diversity 2-21</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>AGCATATTGTGGTGGTGACTGCTATTCC</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22588</th>\n",
       "      <td>124907055</td>\n",
       "      <td>LOC124907055</td>\n",
       "      <td>uncharacterized LOC124907055</td>\n",
       "      <td>ncRNA</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>GGTGGGGTGGGGTGGGGTGGGGTGGGGTGCAGAGAAAACGATTGAT...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22589</th>\n",
       "      <td>106480032</td>\n",
       "      <td>RNU6-1060P</td>\n",
       "      <td>RNA, U6 small nuclear 1060, pseudogene</td>\n",
       "      <td>PSEUDO</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>GTGCTCACTTCAGCAGCACATATACTAAAATTGGAATGATACAGAG...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22590</th>\n",
       "      <td>106481029</td>\n",
       "      <td>RN7SL387P</td>\n",
       "      <td>RNA, 7SL, cytoplasmic 387, pseudogene</td>\n",
       "      <td>PSEUDO</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>GCTGGGCGTGGTGGTGGGTGCCTGTAATCCCAGCTACTAGGGAGGC...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22591</th>\n",
       "      <td>100286918</td>\n",
       "      <td>NDUFS5P2</td>\n",
       "      <td>NADH:ubiquinone oxidoreductase subunit S5 pseu...</td>\n",
       "      <td>PSEUDO</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>TCGTCCTGAAGCAGCGGCCAGAGAAGAGACAAGGGCACGAGCATCA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22592</th>\n",
       "      <td>100189280</td>\n",
       "      <td>TRA-TGC3-1</td>\n",
       "      <td>tRNA-Ala (anticodon TGC) 3-1</td>\n",
       "      <td>tRNA</td>\n",
       "      <td>NCBI Ortholog</td>\n",
       "      <td>GGGGATGTAGCTCAGTGGTAGAGCGCATGCTTTGCATGTATGAGGC...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>22593 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       NCBIGeneID        Symbol  \\\n",
       "0       106481178      RNU4-21P   \n",
       "1       123477792  LOC123477792   \n",
       "2       113174975  LOC113174975   \n",
       "3       116216107  LOC116216107   \n",
       "4           28502      IGHD2-21   \n",
       "...           ...           ...   \n",
       "22588   124907055  LOC124907055   \n",
       "22589   106480032    RNU6-1060P   \n",
       "22590   106481029     RN7SL387P   \n",
       "22591   100286918      NDUFS5P2   \n",
       "22592   100189280    TRA-TGC3-1   \n",
       "\n",
       "                                             Description           GeneType  \\\n",
       "0                   RNA, U4 small nuclear 21, pseudogene             PSEUDO   \n",
       "1                    Sharpr-MPRA regulatory region 12926  BIOLOGICAL_REGION   \n",
       "2                     Sharpr-MPRA regulatory region 7591  BIOLOGICAL_REGION   \n",
       "3      CRISPRi-validated cis-regulatory element chr10...  BIOLOGICAL_REGION   \n",
       "4                    immunoglobulin heavy diversity 2-21              OTHER   \n",
       "...                                                  ...                ...   \n",
       "22588                       uncharacterized LOC124907055              ncRNA   \n",
       "22589             RNA, U6 small nuclear 1060, pseudogene             PSEUDO   \n",
       "22590              RNA, 7SL, cytoplasmic 387, pseudogene             PSEUDO   \n",
       "22591  NADH:ubiquinone oxidoreductase subunit S5 pseu...             PSEUDO   \n",
       "22592                       tRNA-Ala (anticodon TGC) 3-1               tRNA   \n",
       "\n",
       "      GeneGroupMethod                                 NucleotideSequence  \n",
       "0       NCBI Ortholog  AGCTTAGCACAGTGGCAGTATCATAGGCAGTGAGGTTTATCCGAGG...  \n",
       "1       NCBI Ortholog  CTGGAGCGGCCACGATGTGAACTGTCACCGGCCACTGCTGCTCCGA...  \n",
       "2       NCBI Ortholog  TTCCCAATTTTTCCTCTGCTTTTTAATTTTCTAGTTTCCTTTTTCC...  \n",
       "3       NCBI Ortholog  CGCCCAGGCTGGAGTGCAGTGGCGCCATCTCGGCTCACTGCAGGCT...  \n",
       "4       NCBI Ortholog                       AGCATATTGTGGTGGTGACTGCTATTCC  \n",
       "...               ...                                                ...  \n",
       "22588   NCBI Ortholog  GGTGGGGTGGGGTGGGGTGGGGTGGGGTGCAGAGAAAACGATTGAT...  \n",
       "22589   NCBI Ortholog  GTGCTCACTTCAGCAGCACATATACTAAAATTGGAATGATACAGAG...  \n",
       "22590   NCBI Ortholog  GCTGGGCGTGGTGGTGGGTGCCTGTAATCCCAGCTACTAGGGAGGC...  \n",
       "22591   NCBI Ortholog  TCGTCCTGAAGCAGCGGCCAGAGAAGAGACAAGGGCACGAGCATCA...  \n",
       "22592   NCBI Ortholog  GGGGATGTAGCTCAGTGGTAGAGCGCATGCTTTGCATGTATGAGGC...  \n",
       "\n",
       "[22593 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "Add seq len to the dataset, we may need it later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fef67888",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['SequenceLength'] = df['NucleotideSequence'].str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "784a6556",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:21.390672Z",
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   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9a375d5e",
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.countplot(data=df, x='GeneType', palette='viridis', order=df['GeneType'].value_counts().index)\n",
    "plt.title(\"Distribution of Gene Types\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206139d8",
   "metadata": {
    "papermill": {
     "duration": 0.009389,
     "end_time": "2025-07-19T12:48:21.878906",
     "exception": false,
     "start_time": "2025-07-19T12:48:21.869517",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we see, there is severe data imbalances in the dataset, so we will use metric like f1-score as our main metric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17891dc4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:21.899231Z",
     "iopub.status.busy": "2025-07-19T12:48:21.898890Z",
     "iopub.status.idle": "2025-07-19T12:48:22.257794Z",
     "shell.execute_reply": "2025-07-19T12:48:22.256653Z"
    },
    "papermill": {
     "duration": 0.37393,
     "end_time": "2025-07-19T12:48:22.262341",
     "exception": false,
     "start_time": "2025-07-19T12:48:21.888411",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=df, x='GeneType', y='SequenceLength', palette='Set2')\n",
    "plt.title(\"Sequence Length by Gene Type\")\n",
    "plt.ylabel(\"Nucleotide Count\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "752250c8",
   "metadata": {
    "papermill": {
     "duration": 0.07567,
     "end_time": "2025-07-19T12:48:22.349090",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.273420",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 📌 Brief Insights:\n",
    "\n",
    "* **Protein-coding genes** have the **longest sequences**, reflecting their complex structure.\n",
    "* **Pseudogenes** also show **long and variable** sequence lengths.\n",
    "* **Small RNAs** (`tRNA`, `snoRNA`, `snRNA`, etc.) have **short, tightly clustered** lengths.\n",
    "* **Biological regions** show **high variability**, likely due to mixed functional elements.\n",
    "* **ncRNA** and **OTHER** gene types have **diverse lengths**, suggesting heterogeneous content.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dae7e1fb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:22.369795Z",
     "iopub.status.busy": "2025-07-19T12:48:22.369467Z",
     "iopub.status.idle": "2025-07-19T12:48:22.889514Z",
     "shell.execute_reply": "2025-07-19T12:48:22.888615Z"
    },
    "papermill": {
     "duration": 0.532517,
     "end_time": "2025-07-19T12:48:22.891246",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.358729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 5))\n",
    "sns.histplot(df['SequenceLength'], bins=30, kde=True, color='steelblue')\n",
    "plt.title(\"Distribution of DNA Sequence Lengths\")\n",
    "plt.xlabel(\"Length of Sequence\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3abab76a",
   "metadata": {
    "papermill": {
     "duration": 0.010572,
     "end_time": "2025-07-19T12:48:22.913225",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.902653",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### 📌 Brief Insights:\n",
    "\n",
    "* DNA sequence lengths show a **multi-modal distribution**.\n",
    "* Peaks occur around **100–150 bp**, **\\~275 bp**, and **500–600 bp**.\n",
    "* A large spike near **275 bp** suggests a common sequence length, possibly from small RNA or specific gene types.\n",
    "* Longer sequences (>600 bp) are less frequent but still present, likely corresponding to **protein-coding genes** or **pseudogenes**.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a10c1fc9",
   "metadata": {
    "papermill": {
     "duration": 0.01137,
     "end_time": "2025-07-19T12:48:22.935644",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.924274",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da06bd04",
   "metadata": {
    "papermill": {
     "duration": 0.010881,
     "end_time": "2025-07-19T12:48:22.957648",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.946767",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Label encode the GeneType column (target Column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "46a0eefe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:22.981322Z",
     "iopub.status.busy": "2025-07-19T12:48:22.980987Z",
     "iopub.status.idle": "2025-07-19T12:48:22.990469Z",
     "shell.execute_reply": "2025-07-19T12:48:22.989698Z"
    },
    "papermill": {
     "duration": 0.023115,
     "end_time": "2025-07-19T12:48:22.991844",
     "exception": false,
     "start_time": "2025-07-19T12:48:22.968729",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "df['GeneType'] = le.fit_transform(df['GeneType'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b313aeb",
   "metadata": {
    "papermill": {
     "duration": 0.010985,
     "end_time": "2025-07-19T12:48:23.014213",
     "exception": false,
     "start_time": "2025-07-19T12:48:23.003228",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Convert the NucleotideSequence into a valid trainble column. we will convert the the NucleotideSequence into sub-sequences and then use CountVectorizer to convert it into valid trainable column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8b162b8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:23.038627Z",
     "iopub.status.busy": "2025-07-19T12:48:23.037724Z",
     "iopub.status.idle": "2025-07-19T12:48:23.042534Z",
     "shell.execute_reply": "2025-07-19T12:48:23.041695Z"
    },
    "papermill": {
     "duration": 0.018529,
     "end_time": "2025-07-19T12:48:23.044104",
     "exception": false,
     "start_time": "2025-07-19T12:48:23.025575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_kmers(sequence, size=3):\n",
    "    return ' '.join([sequence[i:i+size] for i in range(len(sequence) - size + 1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "321633b2",
   "metadata": {
    "papermill": {
     "duration": 0.011041,
     "end_time": "2025-07-19T12:48:23.067183",
     "exception": false,
     "start_time": "2025-07-19T12:48:23.056142",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0522190f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:23.090899Z",
     "iopub.status.busy": "2025-07-19T12:48:23.090593Z",
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     "shell.execute_reply": "2025-07-19T12:48:23.095068Z"
    },
    "papermill": {
     "duration": 0.01902,
     "end_time": "2025-07-19T12:48:23.097403",
     "exception": false,
     "start_time": "2025-07-19T12:48:23.078383",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ASS SSR SRB RBE BER ERV RVA VAD ADG DGB GBG BGT GTT TTT TTE TEW EWW WWQ WQQ QQE QEE EEE EEE EEE EEE'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_kmers('ASSRBERVADGBGTTTEWWQQEEEEEE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5da13601",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:23.124286Z",
     "iopub.status.busy": "2025-07-19T12:48:23.123459Z",
     "iopub.status.idle": "2025-07-19T12:48:24.220484Z",
     "shell.execute_reply": "2025-07-19T12:48:24.219601Z"
    },
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     "duration": 1.11154,
     "end_time": "2025-07-19T12:48:24.222160",
     "exception": false,
     "start_time": "2025-07-19T12:48:23.110620",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['Kmers'] = df['NucleotideSequence'].apply(get_kmers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "53299b02",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:24.246289Z",
     "iopub.status.busy": "2025-07-19T12:48:24.245943Z",
     "iopub.status.idle": "2025-07-19T12:48:24.254050Z",
     "shell.execute_reply": "2025-07-19T12:48:24.253261Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        AGC GCT CTT TTA TAG AGC GCA CAC ACA CAG AGT GT...\n",
       "1        CTG TGG GGA GAG AGC GCG CGG GGC GCC CCA CAC AC...\n",
       "2        TTC TCC CCC CCA CAA AAT ATT TTT TTT TTT TTC TC...\n",
       "3        CGC GCC CCC CCA CAG AGG GGC GCT CTG TGG GGA GA...\n",
       "4        AGC GCA CAT ATA TAT ATT TTG TGT GTG TGG GGT GT...\n",
       "                               ...                        \n",
       "22588    GGT GTG TGG GGG GGG GGT GTG TGG GGG GGG GGT GT...\n",
       "22589    GTG TGC GCT CTC TCA CAC ACT CTT TTC TCA CAG AG...\n",
       "22590    GCT CTG TGG GGG GGC GCG CGT GTG TGG GGT GTG TG...\n",
       "22591    TCG CGT GTC TCC CCT CTG TGA GAA AAG AGC GCA CA...\n",
       "22592    GGG GGG GGA GAT ATG TGT GTA TAG AGC GCT CTC TC...\n",
       "Name: Kmers, Length: 22593, dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Kmers']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "26a45fd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:24.281684Z",
     "iopub.status.busy": "2025-07-19T12:48:24.280706Z",
     "iopub.status.idle": "2025-07-19T12:48:28.181576Z",
     "shell.execute_reply": "2025-07-19T12:48:28.180721Z"
    },
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     "duration": 3.915162,
     "end_time": "2025-07-19T12:48:28.183199",
     "exception": false,
     "start_time": "2025-07-19T12:48:24.268037",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "X_kmers = vectorizer.fit_transform(df['Kmers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1fd6ee74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.206921Z",
     "iopub.status.busy": "2025-07-19T12:48:28.206624Z",
     "iopub.status.idle": "2025-07-19T12:48:28.363852Z",
     "shell.execute_reply": "2025-07-19T12:48:28.363066Z"
    },
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     "duration": 0.171077,
     "end_time": "2025-07-19T12:48:28.365657",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.194580",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(max_features=100)\n",
    "X_desc = tfidf.fit_transform(df['Description'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39a05acf",
   "metadata": {
    "papermill": {
     "duration": 0.011171,
     "end_time": "2025-07-19T12:48:28.388737",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.377566",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "One Hot Encode the GeneGroupMethod column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c2ffa34c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.413004Z",
     "iopub.status.busy": "2025-07-19T12:48:28.412665Z",
     "iopub.status.idle": "2025-07-19T12:48:28.424876Z",
     "shell.execute_reply": "2025-07-19T12:48:28.424140Z"
    },
    "papermill": {
     "duration": 0.026007,
     "end_time": "2025-07-19T12:48:28.426288",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.400281",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ohe = OneHotEncoder()\n",
    "X_method = ohe.fit_transform(df[['GeneGroupMethod']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a3c0c7",
   "metadata": {
    "papermill": {
     "duration": 0.011369,
     "end_time": "2025-07-19T12:48:28.449210",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.437841",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Stack the columns together using hstack (horizantal stack)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bf66eb9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.473942Z",
     "iopub.status.busy": "2025-07-19T12:48:28.473634Z",
     "iopub.status.idle": "2025-07-19T12:48:28.510889Z",
     "shell.execute_reply": "2025-07-19T12:48:28.509966Z"
    },
    "papermill": {
     "duration": 0.051364,
     "end_time": "2025-07-19T12:48:28.512538",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.461174",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_final = hstack([\n",
    "    X_kmers,\n",
    "    X_method,\n",
    "    X_desc,\n",
    "    df[['SequenceLength']]\n",
    "])\n",
    "\n",
    "\n",
    "y = df['GeneType']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77aec79b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.536908Z",
     "iopub.status.busy": "2025-07-19T12:48:28.536608Z",
     "iopub.status.idle": "2025-07-19T12:48:28.603550Z",
     "shell.execute_reply": "2025-07-19T12:48:28.602758Z"
    },
    "papermill": {
     "duration": 0.081003,
     "end_time": "2025-07-19T12:48:28.605178",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.524175",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X_final, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f34091d0",
   "metadata": {
    "papermill": {
     "duration": 0.011309,
     "end_time": "2025-07-19T12:48:28.627955",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.616646",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "735fc5f1",
   "metadata": {
    "papermill": {
     "duration": 0.011428,
     "end_time": "2025-07-19T12:48:28.650908",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.639480",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Test different models and select the best model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "90ea36c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.674760Z",
     "iopub.status.busy": "2025-07-19T12:48:28.674451Z",
     "iopub.status.idle": "2025-07-19T12:48:28.678930Z",
     "shell.execute_reply": "2025-07-19T12:48:28.678258Z"
    },
    "papermill": {
     "duration": 0.018103,
     "end_time": "2025-07-19T12:48:28.680196",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.662093",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "models = {\n",
    "    \"Logistic Regression\": LogisticRegression(max_iter=1000),\n",
    "    \"Random Forest\": RandomForestClassifier(),\n",
    "    \"SVM\": SVC(),\n",
    "    \"Naive Bayes\": MultinomialNB(),\n",
    "    \"KNN\": KNeighborsClassifier(),\n",
    "    \"Gradient Boosting\": GradientBoostingClassifier(),\n",
    "    \"Decision Tree\": DecisionTreeClassifier()\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4c096994",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:48:28.704595Z",
     "iopub.status.busy": "2025-07-19T12:48:28.704263Z",
     "iopub.status.idle": "2025-07-19T12:56:02.488857Z",
     "shell.execute_reply": "2025-07-19T12:56:02.487851Z"
    },
    "papermill": {
     "duration": 453.798948,
     "end_time": "2025-07-19T12:56:02.490837",
     "exception": false,
     "start_time": "2025-07-19T12:48:28.691889",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.11/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. OF ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1344: 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, msg_start, len(result))\n",
      "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1344: 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, msg_start, len(result))\n",
      "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1344: 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, msg_start, len(result))\n",
      "/usr/local/lib/python3.11/dist-packages/sklearn/metrics/_classification.py:1344: 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, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    preds = model.predict(X_test)\n",
    "    results.append({\n",
    "        \"Model\": name,\n",
    "        \"Accuracy\": accuracy_score(y_test, preds),\n",
    "        \"Precision\": precision_score(y_test, preds, average='macro'),\n",
    "        \"Recall\": recall_score(y_test, preds, average='macro'),\n",
    "        \"F1 Score\": f1_score(y_test, preds, average='macro')\n",
    "    })\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "8543820d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:56:02.516154Z",
     "iopub.status.busy": "2025-07-19T12:56:02.515781Z",
     "iopub.status.idle": "2025-07-19T12:56:02.522127Z",
     "shell.execute_reply": "2025-07-19T12:56:02.521190Z"
    },
    "papermill": {
     "duration": 0.020448,
     "end_time": "2025-07-19T12:56:02.523507",
     "exception": false,
     "start_time": "2025-07-19T12:56:02.503059",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'Model': 'Logistic Regression',\n",
       "  'Accuracy': 0.9245408276167294,\n",
       "  'Precision': 0.7402177896311077,\n",
       "  'Recall': 0.6656989105521498,\n",
       "  'F1 Score': 0.6881335572660581},\n",
       " {'Model': 'Random Forest',\n",
       "  'Accuracy': 0.9800840894003098,\n",
       "  'Precision': 0.9632950847128168,\n",
       "  'Recall': 0.9504534475240624,\n",
       "  'F1 Score': 0.955137149647207},\n",
       " {'Model': 'SVM',\n",
       "  'Accuracy': 0.6552334587298074,\n",
       "  'Precision': 0.18406328298134816,\n",
       "  'Recall': 0.20863958079992492,\n",
       "  'F1 Score': 0.19543313683143937},\n",
       " {'Model': 'Naive Bayes',\n",
       "  'Accuracy': 0.7178579331710555,\n",
       "  'Precision': 0.48576183937305883,\n",
       "  'Recall': 0.6833238851732897,\n",
       "  'F1 Score': 0.5197836615857548},\n",
       " {'Model': 'KNN',\n",
       "  'Accuracy': 0.8404514273069263,\n",
       "  'Precision': 0.7178343401858773,\n",
       "  'Recall': 0.6742084304936032,\n",
       "  'F1 Score': 0.6883337090061303},\n",
       " {'Model': 'Gradient Boosting',\n",
       "  'Accuracy': 0.9836246957291436,\n",
       "  'Precision': 0.9601370440611611,\n",
       "  'Recall': 0.9749661935435503,\n",
       "  'F1 Score': 0.9669105173198002},\n",
       " {'Model': 'Decision Tree',\n",
       "  'Accuracy': 0.978535074131445,\n",
       "  'Precision': 0.9509669613669811,\n",
       "  'Recall': 0.9558577679989678,\n",
       "  'F1 Score': 0.9525796996233277}]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "66049958",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:56:02.548736Z",
     "iopub.status.busy": "2025-07-19T12:56:02.548406Z",
     "iopub.status.idle": "2025-07-19T12:56:02.561663Z",
     "shell.execute_reply": "2025-07-19T12:56:02.560628Z"
    },
    "papermill": {
     "duration": 0.027573,
     "end_time": "2025-07-19T12:56:02.563068",
     "exception": false,
     "start_time": "2025-07-19T12:56:02.535495",
     "status": "completed"
    },
    "tags": []
   },
   "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>Model</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1 Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.983625</td>\n",
       "      <td>0.960137</td>\n",
       "      <td>0.974966</td>\n",
       "      <td>0.966911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.980084</td>\n",
       "      <td>0.963295</td>\n",
       "      <td>0.950453</td>\n",
       "      <td>0.955137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.978535</td>\n",
       "      <td>0.950967</td>\n",
       "      <td>0.955858</td>\n",
       "      <td>0.952580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNN</td>\n",
       "      <td>0.840451</td>\n",
       "      <td>0.717834</td>\n",
       "      <td>0.674208</td>\n",
       "      <td>0.688334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>0.924541</td>\n",
       "      <td>0.740218</td>\n",
       "      <td>0.665699</td>\n",
       "      <td>0.688134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>0.717858</td>\n",
       "      <td>0.485762</td>\n",
       "      <td>0.683324</td>\n",
       "      <td>0.519784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SVM</td>\n",
       "      <td>0.655233</td>\n",
       "      <td>0.184063</td>\n",
       "      <td>0.208640</td>\n",
       "      <td>0.195433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model  Accuracy  Precision    Recall  F1 Score\n",
       "0    Gradient Boosting  0.983625   0.960137  0.974966  0.966911\n",
       "1        Random Forest  0.980084   0.963295  0.950453  0.955137\n",
       "2        Decision Tree  0.978535   0.950967  0.955858  0.952580\n",
       "3                  KNN  0.840451   0.717834  0.674208  0.688334\n",
       "4  Logistic Regression  0.924541   0.740218  0.665699  0.688134\n",
       "5          Naive Bayes  0.717858   0.485762  0.683324  0.519784\n",
       "6                  SVM  0.655233   0.184063  0.208640  0.195433"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_df = pd.DataFrame(results)\n",
    "results_df.sort_values(by=\"F1 Score\", ascending=False, inplace=True)\n",
    "results_df.reset_index(drop=True, inplace=True)\n",
    "results_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bfbcc4b",
   "metadata": {
    "papermill": {
     "duration": 0.011651,
     "end_time": "2025-07-19T12:56:02.586835",
     "exception": false,
     "start_time": "2025-07-19T12:56:02.575184",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "As we can see, the GradientBoostingClassifier performs so good on the data, so we will stick with it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "290c2f8a",
   "metadata": {
    "papermill": {
     "duration": 0.011719,
     "end_time": "2025-07-19T12:56:02.610882",
     "exception": false,
     "start_time": "2025-07-19T12:56:02.599163",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91c46c74",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:56:02.636479Z",
     "iopub.status.busy": "2025-07-19T12:56:02.636148Z",
     "iopub.status.idle": "2025-07-19T12:58:31.605594Z",
     "shell.execute_reply": "2025-07-19T12:58:31.604596Z"
    },
    "papermill": {
     "duration": 148.996648,
     "end_time": "2025-07-19T12:58:31.619328",
     "exception": false,
     "start_time": "2025-07-19T12:56:02.622680",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GradientBoostingClassifier()</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 sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "GradientBoostingClassifier()"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb_model = GradientBoostingClassifier()\n",
    "gb_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31968fdc",
   "metadata": {
    "papermill": {
     "duration": 0.01197,
     "end_time": "2025-07-19T12:58:31.643608",
     "exception": false,
     "start_time": "2025-07-19T12:58:31.631638",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Model Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3269fb21",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:58:31.670126Z",
     "iopub.status.busy": "2025-07-19T12:58:31.669761Z",
     "iopub.status.idle": "2025-07-19T12:58:31.797711Z",
     "shell.execute_reply": "2025-07-19T12:58:31.796905Z"
    },
    "papermill": {
     "duration": 0.14315,
     "end_time": "2025-07-19T12:58:31.799404",
     "exception": false,
     "start_time": "2025-07-19T12:58:31.656254",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = gb_model.predict(X_test)\n",
    "\n",
    "# Compute metrics\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='macro')\n",
    "recall = recall_score(y_test, y_pred, average='macro')\n",
    "f1 = f1_score(y_test, y_pred, average='macro')\n",
    "cm = confusion_matrix(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "072af57e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:58:31.825406Z",
     "iopub.status.busy": "2025-07-19T12:58:31.825120Z",
     "iopub.status.idle": "2025-07-19T12:58:31.830359Z",
     "shell.execute_reply": "2025-07-19T12:58:31.829503Z"
    },
    "papermill": {
     "duration": 0.01994,
     "end_time": "2025-07-19T12:58:31.831878",
     "exception": false,
     "start_time": "2025-07-19T12:58:31.811938",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gradient Boosting Classifier Performance:\n",
      "Accuracy : 0.9832\n",
      "Precision: 0.9590\n",
      "Recall   : 0.9702\n",
      "F1 Score : 0.9641\n"
     ]
    }
   ],
   "source": [
    "print(\"Gradient Boosting Classifier Performance:\")\n",
    "print(f\"Accuracy : {accuracy:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall   : {recall:.4f}\")\n",
    "print(f\"F1 Score : {f1:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "46585b5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:58:31.857952Z",
     "iopub.status.busy": "2025-07-19T12:58:31.857626Z",
     "iopub.status.idle": "2025-07-19T12:58:32.403951Z",
     "shell.execute_reply": "2025-07-19T12:58:32.402901Z"
    },
    "papermill": {
     "duration": 0.560893,
     "end_time": "2025-07-19T12:58:32.405490",
     "exception": false,
     "start_time": "2025-07-19T12:58:31.844597",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 8))\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=gb_model.classes_)\n",
    "disp.plot(cmap=\"Blues\", ax=plt.gca())\n",
    "plt.title(\"Confusion Matrix - Gradient Boosting\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c08fe25f",
   "metadata": {
    "papermill": {
     "duration": 0.013947,
     "end_time": "2025-07-19T12:58:32.433595",
     "exception": false,
     "start_time": "2025-07-19T12:58:32.419648",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Save The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1e5207cb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-07-19T12:58:32.463619Z",
     "iopub.status.busy": "2025-07-19T12:58:32.462857Z",
     "iopub.status.idle": "2025-07-19T12:58:32.498666Z",
     "shell.execute_reply": "2025-07-19T12:58:32.497598Z"
    },
    "papermill": {
     "duration": 0.052575,
     "end_time": "2025-07-19T12:58:32.500287",
     "exception": false,
     "start_time": "2025-07-19T12:58:32.447712",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gradient_boosting_model.pkl']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "joblib.dump(gb_model, 'gradient_boosting_model.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d0e02a",
   "metadata": {
    "papermill": {
     "duration": 0.013646,
     "end_time": "2025-07-19T12:58:32.528627",
     "exception": false,
     "start_time": "2025-07-19T12:58:32.514981",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Thank You"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb2a1d97",
   "metadata": {
    "papermill": {
     "duration": 0.013832,
     "end_time": "2025-07-19T12:58:32.556545",
     "exception": false,
     "start_time": "2025-07-19T12:58:32.542713",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 7823994,
     "sourceId": 12406681,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 31089,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "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": "3.11.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 628.316822,
   "end_time": "2025-07-19T12:58:33.693550",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2025-07-19T12:48:05.376728",
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