From 48f5823785e71444558f0f296f30e52cc623d777 Mon Sep 17 00:00:00 2001
From: arsalanyavari <arya48.yavari79@gmail.com>
Date: Thu, 26 Sep 2024 17:12:29 +0330
Subject: [PATCH] add ML naive bayes and random forrest

---
 ML/classification/naive bayes/.gitignore      |   3 +
 .../naive bayes/naive_bayes.ipynb             | 803 +++++++++++++++++
 ML/classification/random forrest/.gitignore   |   3 +
 ML/classification/random forrest/.gitkeep     |   0
 .../random forrest/dataset/archive.zip        | Bin 0 -> 4963 bytes
 .../random forrest/random_forest.ipynb        | 814 ++++++++++++++++++
 6 files changed, 1623 insertions(+)
 create mode 100644 ML/classification/naive bayes/.gitignore
 create mode 100644 ML/classification/naive bayes/naive_bayes.ipynb
 create mode 100644 ML/classification/random forrest/.gitignore
 create mode 100644 ML/classification/random forrest/.gitkeep
 create mode 100644 ML/classification/random forrest/dataset/archive.zip
 create mode 100644 ML/classification/random forrest/random_forest.ipynb

diff --git a/ML/classification/naive bayes/.gitignore b/ML/classification/naive bayes/.gitignore
new file mode 100644
index 0000000..849d8e6
--- /dev/null
+++ b/ML/classification/naive bayes/.gitignore	
@@ -0,0 +1,3 @@
+*.csv
+.ipynb_checkpoints
+
diff --git a/ML/classification/naive bayes/naive_bayes.ipynb b/ML/classification/naive bayes/naive_bayes.ipynb
new file mode 100644
index 0000000..0037d7b
--- /dev/null
+++ b/ML/classification/naive bayes/naive_bayes.ipynb	
@@ -0,0 +1,803 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "29b5381a-72b4-4f81-9208-2075f7acad85",
+   "metadata": {},
+   "source": [
+    "# Naive Bayes Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cac03d32",
+   "metadata": {},
+   "source": [
+    "Configure the project. Indeed you create a dataset in csv format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6f480cda-8380-4355-998a-5c59d6203b05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive:  ./dataset/archive.zip\n",
+      "  inflating: Graduate.csv            \n",
+      "  inflating: play.csv                \n"
+     ]
+    }
+   ],
+   "source": [
+    "! rm -rf *.csv\n",
+    "! unzip ./dataset/archive.zip\n",
+    "! rm play.csv\n",
+    "! mv Graduate.csv data.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52ec2f48",
+   "metadata": {},
+   "source": [
+    "Import needed libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd17f780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, jaccard_score\n",
+    "import scikitplot as skplt\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57b33a77",
+   "metadata": {},
+   "source": [
+    "Read data from data.csv using pandas and store in data frame structure. Also shuffle data to have uniform distribution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a102a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Serial No.</th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>362</td>\n",
+       "      <td>334</td>\n",
+       "      <td>116</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>9.54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>74</td>\n",
+       "      <td>314</td>\n",
+       "      <td>108</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>375</td>\n",
+       "      <td>315</td>\n",
+       "      <td>105</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>156</td>\n",
+       "      <td>312</td>\n",
+       "      <td>109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>105</td>\n",
+       "      <td>326</td>\n",
+       "      <td>112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.05</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
+       "0         362        334          116                  4  4.0   3.5  9.54   \n",
+       "1          74        314          108                  4  4.5   4.0  9.04   \n",
+       "2         375        315          105                  2  2.0   2.5  7.65   \n",
+       "3         156        312          109                  3  3.0   3.0  8.69   \n",
+       "4         105        326          112                  3  3.5   3.0  9.05   \n",
+       "\n",
+       "   Research  Admit  \n",
+       "0         1      1  \n",
+       "1         1      1  \n",
+       "2         0      0  \n",
+       "3         0      1  \n",
+       "4         1      1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data.csv\")\n",
+    "df.head()\n",
+    "df = df.sample(frac=1.0, random_state=42).reset_index(drop=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca6a571f",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6f063a14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(500, 9)\n",
+      "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
+      "       'LOR ', 'CGPA', 'Research', 'Admit'],\n",
+      "      dtype='object')\n",
+      "Serial No.             int64\n",
+      "GRE Score              int64\n",
+      "TOEFL Score            int64\n",
+      "University Rating      int64\n",
+      "SOP                  float64\n",
+      "LOR                  float64\n",
+      "CGPA                 float64\n",
+      "Research               int64\n",
+      "Admit                  int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)\n",
+    "print(df.columns)\n",
+    "print(df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e4bf7dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Admit\n",
+       "1    463\n",
+       "0     37\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Admit'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1743c9c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>334</td>\n",
+       "      <td>116</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>9.54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>314</td>\n",
+       "      <td>108</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315</td>\n",
+       "      <td>105</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>312</td>\n",
+       "      <td>109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>326</td>\n",
+       "      <td>112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.05</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  Admit\n",
+       "0        334          116                  4  4.0   3.5  9.54         1      1\n",
+       "1        314          108                  4  4.5   4.0  9.04         1      1\n",
+       "2        315          105                  2  2.0   2.5  7.65         0      0\n",
+       "3        312          109                  3  3.0   3.0  8.69         0      1\n",
+       "4        326          112                  3  3.5   3.0  9.05         1      1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop('Serial No.', axis=1)\n",
+    "categorical_attr = ['Admit']\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "df[categorical_attr] = df[categorical_attr].apply(le.fit_transform, axis=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "93002df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.00000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>316.472000</td>\n",
+       "      <td>107.192000</td>\n",
+       "      <td>3.114000</td>\n",
+       "      <td>3.374000</td>\n",
+       "      <td>3.48400</td>\n",
+       "      <td>8.576440</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.926000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.295148</td>\n",
+       "      <td>6.081868</td>\n",
+       "      <td>1.143512</td>\n",
+       "      <td>0.991004</td>\n",
+       "      <td>0.92545</td>\n",
+       "      <td>0.604813</td>\n",
+       "      <td>0.496884</td>\n",
+       "      <td>0.262033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>6.800000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>308.000000</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>8.127500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>317.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>3.50000</td>\n",
+       "      <td>8.560000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>325.000000</td>\n",
+       "      <td>112.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>9.040000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>340.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.00000</td>\n",
+       "      <td>9.920000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        GRE Score  TOEFL Score  University Rating         SOP       LOR   \\\n",
+       "count  500.000000   500.000000         500.000000  500.000000  500.00000   \n",
+       "mean   316.472000   107.192000           3.114000    3.374000    3.48400   \n",
+       "std     11.295148     6.081868           1.143512    0.991004    0.92545   \n",
+       "min    290.000000    92.000000           1.000000    1.000000    1.00000   \n",
+       "25%    308.000000   103.000000           2.000000    2.500000    3.00000   \n",
+       "50%    317.000000   107.000000           3.000000    3.500000    3.50000   \n",
+       "75%    325.000000   112.000000           4.000000    4.000000    4.00000   \n",
+       "max    340.000000   120.000000           5.000000    5.000000    5.00000   \n",
+       "\n",
+       "             CGPA    Research       Admit  \n",
+       "count  500.000000  500.000000  500.000000  \n",
+       "mean     8.576440    0.560000    0.926000  \n",
+       "std      0.604813    0.496884    0.262033  \n",
+       "min      6.800000    0.000000    0.000000  \n",
+       "25%      8.127500    0.000000    1.000000  \n",
+       "50%      8.560000    1.000000    1.000000  \n",
+       "75%      9.040000    1.000000    1.000000  \n",
+       "max      9.920000    1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarize data\n",
+    "df.describe() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ebceb4d",
+   "metadata": {},
+   "source": [
+    "Print the histogram chart of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "834eb664",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
+      "       'Research', 'Admit'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "39faae37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x700 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "viz = df[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research', 'Admit']]\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 7))\n",
+    "\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, column in enumerate(viz.columns):\n",
+    "    viz[column].hist(ax=axes[i])\n",
+    "    axes[i].set_title(column)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0068030a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(df)\n",
+    "train, test = train_test_split(df, test_size=0.20, random_state=42)\n",
+    "# test, evaluate = train_test_split(test, test_size=0.5, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70a01055",
+   "metadata": {},
+   "source": [
+    "## Fit model based on data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "92f95186",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x = np.asanyarray(train[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research']])\n",
+    "train_y = np.asanyarray(train[['Admit']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a3bb7499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_x = np.asanyarray(test[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research']])\n",
+    "test_y = np.asanyarray(test[['Admit']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "bce9d88c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = GaussianNB()\n",
+    "model.fit(train_x, train_y.ravel())\n",
+    "test_y_ = model.predict(test_x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43eafda9",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "c31aad30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The jaccard score for 0's:\n",
+      "0.30434782608695654\n",
+      "\n",
+      "The jaccard score for 1's:\n",
+      "0.8279569892473119\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The jaccard score for 0's:\")\n",
+    "print(jaccard_score(test_y, test_y_, pos_label=0))\n",
+    "print(\"\\nThe jaccard score for 1's:\")\n",
+    "print(jaccard_score(test_y, test_y_, pos_label=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "150897f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Accuracy: 0.84\n",
+      "Precision: 0.99\n",
+      "Recall: 0.84\n",
+      "\n",
+      "Naive Bayes Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.32      0.88      0.47         8\n",
+      "           1       0.99      0.84      0.91        92\n",
+      "\n",
+      "    accuracy                           0.84       100\n",
+      "   macro avg       0.65      0.86      0.69       100\n",
+      "weighted avg       0.93      0.84      0.87       100\n",
+      "\n",
+      "\n",
+      "Naive Bayes Confusion Matrix:\n",
+      "[[ 7  1]\n",
+      " [15 77]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accuracy = accuracy_score(test_y, test_y_)\n",
+    "precision = precision_score(test_y, test_y_)\n",
+    "recall = recall_score(test_y, test_y_)\n",
+    "\n",
+    "print(f\"\\nAccuracy: {accuracy:.2f}\")\n",
+    "print(f\"Precision: {precision:.2f}\")\n",
+    "print(f\"Recall: {recall:.2f}\")\n",
+    "\n",
+    "# Classification report and confusion matrix\n",
+    "print(\"\\nNaive Bayes Classification Report:\")\n",
+    "print(classification_report(test_y, test_y_))\n",
+    "\n",
+    "print(\"\\nNaive Bayes Confusion Matrix:\")\n",
+    "print(confusion_matrix(test_y, test_y_))\n",
+    "\n",
+    "# Plot Confusion Matrix\n",
+    "skplt.metrics.plot_confusion_matrix(test_y, test_y_)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e8003fa",
+   "metadata": {},
+   "source": [
+    "Lets solve it using random forest"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "venv",
+   "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": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/ML/classification/random forrest/.gitignore b/ML/classification/random forrest/.gitignore
new file mode 100644
index 0000000..849d8e6
--- /dev/null
+++ b/ML/classification/random forrest/.gitignore	
@@ -0,0 +1,3 @@
+*.csv
+.ipynb_checkpoints
+
diff --git a/ML/classification/random forrest/.gitkeep b/ML/classification/random forrest/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/ML/classification/random forrest/dataset/archive.zip b/ML/classification/random forrest/dataset/archive.zip
new file mode 100644
index 0000000000000000000000000000000000000000..1809c4186d5d6649c10b6fd38e96cd67264e504d
GIT binary patch
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diff --git a/ML/classification/random forrest/random_forest.ipynb b/ML/classification/random forrest/random_forest.ipynb
new file mode 100644
index 0000000..287ffeb
--- /dev/null
+++ b/ML/classification/random forrest/random_forest.ipynb	
@@ -0,0 +1,814 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "29b5381a-72b4-4f81-9208-2075f7acad85",
+   "metadata": {},
+   "source": [
+    "# Random Forest Classification"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cac03d32",
+   "metadata": {},
+   "source": [
+    "Configure the project. Indeed you create a dataset in csv format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6f480cda-8380-4355-998a-5c59d6203b05",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive:  ./dataset/archive.zip\n",
+      "  inflating: Graduate.csv            \n",
+      "  inflating: play.csv                \n"
+     ]
+    }
+   ],
+   "source": [
+    "! rm -rf *.csv\n",
+    "! unzip ./dataset/archive.zip\n",
+    "! rm play.csv\n",
+    "! mv Graduate.csv data.csv"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52ec2f48",
+   "metadata": {},
+   "source": [
+    "Import needed libraries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dd17f780",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score, precision_score, recall_score, jaccard_score\n",
+    "import scikitplot as skplt\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57b33a77",
+   "metadata": {},
+   "source": [
+    "Read data from data.csv using pandas and store in data frame structure. Also shuffle data to have uniform distribution. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a102a751",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Serial No.</th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>362</td>\n",
+       "      <td>334</td>\n",
+       "      <td>116</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>9.54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>74</td>\n",
+       "      <td>314</td>\n",
+       "      <td>108</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>375</td>\n",
+       "      <td>315</td>\n",
+       "      <td>105</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>156</td>\n",
+       "      <td>312</td>\n",
+       "      <td>109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>105</td>\n",
+       "      <td>326</td>\n",
+       "      <td>112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.05</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Serial No.  GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  \\\n",
+       "0         362        334          116                  4  4.0   3.5  9.54   \n",
+       "1          74        314          108                  4  4.5   4.0  9.04   \n",
+       "2         375        315          105                  2  2.0   2.5  7.65   \n",
+       "3         156        312          109                  3  3.0   3.0  8.69   \n",
+       "4         105        326          112                  3  3.5   3.0  9.05   \n",
+       "\n",
+       "   Research  Admit  \n",
+       "0         1      1  \n",
+       "1         1      1  \n",
+       "2         0      0  \n",
+       "3         0      1  \n",
+       "4         1      1  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"data.csv\")\n",
+    "df.head()\n",
+    "df = df.sample(frac=1.0, random_state=42).reset_index(drop=True)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca6a571f",
+   "metadata": {},
+   "source": [
+    "## Preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "6f063a14",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(500, 9)\n",
+      "Index(['Serial No.', 'GRE Score', 'TOEFL Score', 'University Rating', 'SOP',\n",
+      "       'LOR ', 'CGPA', 'Research', 'Admit'],\n",
+      "      dtype='object')\n",
+      "Serial No.             int64\n",
+      "GRE Score              int64\n",
+      "TOEFL Score            int64\n",
+      "University Rating      int64\n",
+      "SOP                  float64\n",
+      "LOR                  float64\n",
+      "CGPA                 float64\n",
+      "Research               int64\n",
+      "Admit                  int64\n",
+      "dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.shape)\n",
+    "print(df.columns)\n",
+    "print(df.dtypes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e4bf7dcd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Admit\n",
+       "1    463\n",
+       "0     37\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Admit'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "1743c9c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>334</td>\n",
+       "      <td>116</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>9.54</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>314</td>\n",
+       "      <td>108</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4.5</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>9.04</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>315</td>\n",
+       "      <td>105</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>2.5</td>\n",
+       "      <td>7.65</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>312</td>\n",
+       "      <td>109</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.69</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>326</td>\n",
+       "      <td>112</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>9.05</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   GRE Score  TOEFL Score  University Rating  SOP  LOR   CGPA  Research  Admit\n",
+       "0        334          116                  4  4.0   3.5  9.54         1      1\n",
+       "1        314          108                  4  4.5   4.0  9.04         1      1\n",
+       "2        315          105                  2  2.0   2.5  7.65         0      0\n",
+       "3        312          109                  3  3.0   3.0  8.69         0      1\n",
+       "4        326          112                  3  3.5   3.0  9.05         1      1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df.drop('Serial No.', axis=1)\n",
+    "categorical_attr = ['Admit']\n",
+    "\n",
+    "le = LabelEncoder()\n",
+    "df[categorical_attr] = df[categorical_attr].apply(le.fit_transform, axis=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "93002df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>GRE Score</th>\n",
+       "      <th>TOEFL Score</th>\n",
+       "      <th>University Rating</th>\n",
+       "      <th>SOP</th>\n",
+       "      <th>LOR</th>\n",
+       "      <th>CGPA</th>\n",
+       "      <th>Research</th>\n",
+       "      <th>Admit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.00000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "      <td>500.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>316.472000</td>\n",
+       "      <td>107.192000</td>\n",
+       "      <td>3.114000</td>\n",
+       "      <td>3.374000</td>\n",
+       "      <td>3.48400</td>\n",
+       "      <td>8.576440</td>\n",
+       "      <td>0.560000</td>\n",
+       "      <td>0.926000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>11.295148</td>\n",
+       "      <td>6.081868</td>\n",
+       "      <td>1.143512</td>\n",
+       "      <td>0.991004</td>\n",
+       "      <td>0.92545</td>\n",
+       "      <td>0.604813</td>\n",
+       "      <td>0.496884</td>\n",
+       "      <td>0.262033</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>290.000000</td>\n",
+       "      <td>92.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.00000</td>\n",
+       "      <td>6.800000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>308.000000</td>\n",
+       "      <td>103.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.500000</td>\n",
+       "      <td>3.00000</td>\n",
+       "      <td>8.127500</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>317.000000</td>\n",
+       "      <td>107.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.500000</td>\n",
+       "      <td>3.50000</td>\n",
+       "      <td>8.560000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>325.000000</td>\n",
+       "      <td>112.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>4.00000</td>\n",
+       "      <td>9.040000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>340.000000</td>\n",
+       "      <td>120.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>5.00000</td>\n",
+       "      <td>9.920000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        GRE Score  TOEFL Score  University Rating         SOP       LOR   \\\n",
+       "count  500.000000   500.000000         500.000000  500.000000  500.00000   \n",
+       "mean   316.472000   107.192000           3.114000    3.374000    3.48400   \n",
+       "std     11.295148     6.081868           1.143512    0.991004    0.92545   \n",
+       "min    290.000000    92.000000           1.000000    1.000000    1.00000   \n",
+       "25%    308.000000   103.000000           2.000000    2.500000    3.00000   \n",
+       "50%    317.000000   107.000000           3.000000    3.500000    3.50000   \n",
+       "75%    325.000000   112.000000           4.000000    4.000000    4.00000   \n",
+       "max    340.000000   120.000000           5.000000    5.000000    5.00000   \n",
+       "\n",
+       "             CGPA    Research       Admit  \n",
+       "count  500.000000  500.000000  500.000000  \n",
+       "mean     8.576440    0.560000    0.926000  \n",
+       "std      0.604813    0.496884    0.262033  \n",
+       "min      6.800000    0.000000    0.000000  \n",
+       "25%      8.127500    0.000000    1.000000  \n",
+       "50%      8.560000    1.000000    1.000000  \n",
+       "75%      9.040000    1.000000    1.000000  \n",
+       "max      9.920000    1.000000    1.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# summarize data\n",
+    "df.describe() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ebceb4d",
+   "metadata": {},
+   "source": [
+    "Print the histogram chart of data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "834eb664",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA',\n",
+      "       'Research', 'Admit'],\n",
+      "      dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "39faae37",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2000x700 with 8 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "viz = df[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research', 'Admit']]\n",
+    "fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(20, 7))\n",
+    "\n",
+    "axes = axes.flatten()\n",
+    "\n",
+    "for i, column in enumerate(viz.columns):\n",
+    "    viz[column].hist(ax=axes[i])\n",
+    "    axes[i].set_title(column)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "0068030a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# print(df)\n",
+    "train, test = train_test_split(df, test_size=0.20, random_state=42)\n",
+    "# test, evaluate = train_test_split(test, test_size=0.5, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70a01055",
+   "metadata": {},
+   "source": [
+    "## Fit model based on data. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "92f95186",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_x = np.asanyarray(train[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research']])\n",
+    "train_y = np.asanyarray(train[['Admit']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a3bb7499",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_x = np.asanyarray(test[['GRE Score', 'TOEFL Score', 'University Rating', 'SOP', 'LOR ', 'CGPA', 'Research']])\n",
+    "test_y = np.asanyarray(test[['Admit']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "10d47b93",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Scale features\n",
+    "scaler = StandardScaler()\n",
+    "train_x = scaler.fit_transform(train_x)\n",
+    "test_x = scaler.transform(test_x)\n",
+    "\n",
+    "# Handle class imbalance with SMOTE\n",
+    "smote = SMOTE(random_state=42)\n",
+    "train_x_resampled, train_y_resampled = smote.fit_resample(train_x, train_y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bce9d88c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = RandomForestClassifier(random_state=42)\n",
+    "model.fit(train_x_resampled, train_y_resampled.ravel())\n",
+    "test_y_ = model.predict(test_x)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43eafda9",
+   "metadata": {},
+   "source": [
+    "## Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c31aad30",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The jaccard score for 0's:\n",
+      "0.26666666666666666\n",
+      "\n",
+      "The jaccard score for 1's:\n",
+      "0.8854166666666666\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"The jaccard score for 0's:\")\n",
+    "print(jaccard_score(test_y, test_y_, pos_label=0))\n",
+    "print(\"\\nThe jaccard score for 1's:\")\n",
+    "print(jaccard_score(test_y, test_y_, pos_label=1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "150897f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Accuracy: 0.89\n",
+      "Precision: 0.96\n",
+      "Recall: 0.92\n",
+      "\n",
+      "Naive Bayes Classification Report:\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.36      0.50      0.42         8\n",
+      "           1       0.96      0.92      0.94        92\n",
+      "\n",
+      "    accuracy                           0.89       100\n",
+      "   macro avg       0.66      0.71      0.68       100\n",
+      "weighted avg       0.91      0.89      0.90       100\n",
+      "\n",
+      "\n",
+      "Naive Bayes Confusion Matrix:\n",
+      "[[ 4  4]\n",
+      " [ 7 85]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: title={'center': 'Confusion Matrix'}, xlabel='Predicted label', ylabel='True label'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "accuracy = accuracy_score(test_y, test_y_)\n",
+    "precision = precision_score(test_y, test_y_)\n",
+    "recall = recall_score(test_y, test_y_)\n",
+    "\n",
+    "print(f\"\\nAccuracy: {accuracy:.2f}\")\n",
+    "print(f\"Precision: {precision:.2f}\")\n",
+    "print(f\"Recall: {recall:.2f}\")\n",
+    "\n",
+    "# Classification report and confusion matrix\n",
+    "print(\"\\nNaive Bayes Classification Report:\")\n",
+    "print(classification_report(test_y, test_y_))\n",
+    "\n",
+    "print(\"\\nNaive Bayes Confusion Matrix:\")\n",
+    "print(confusion_matrix(test_y, test_y_))\n",
+    "\n",
+    "# Plot Confusion Matrix\n",
+    "skplt.metrics.plot_confusion_matrix(test_y, test_y_)"
+   ]
+  }
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