diff --git "a/Building an Enhanced Movie Recommendation System Using Machine Learning.ipynb" "b/Building an Enhanced Movie Recommendation System Using Machine Learning.ipynb"
new file mode 100644--- /dev/null
+++ "b/Building an Enhanced Movie Recommendation System Using Machine Learning.ipynb"
@@ -0,0 +1,881 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2d2000ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's start by loading some packages\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings \n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "77b2ca5e",
+   "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>userId</th>\n",
+       "      <th>movieId</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2005-04-02 23:53:47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2005-04-02 23:31:16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>32</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2005-04-02 23:33:39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>47</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2005-04-02 23:32:07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>50</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2005-04-02 23:29:40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   userId  movieId  rating            timestamp\n",
+       "0       1        2     3.5  2005-04-02 23:53:47\n",
+       "1       1       29     3.5  2005-04-02 23:31:16\n",
+       "2       1       32     3.5  2005-04-02 23:33:39\n",
+       "3       1       47     3.5  2005-04-02 23:32:07\n",
+       "4       1       50     3.5  2005-04-02 23:29:40"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "review = pd.read_csv('rating.csv')\n",
+    "review.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a8d96a10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "userId       0\n",
+       "movieId      0\n",
+       "rating       0\n",
+       "timestamp    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "review.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ed2fbf6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "userId       False\n",
+       "movieId      False\n",
+       "rating       False\n",
+       "timestamp    False\n",
+       "dtype: bool"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "review.isna().any()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "498b9c4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(20000263, 4)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "review.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0c9e691e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total Number of Unique Users Ids:  138493\n",
+      "Total Number of Unique Movie Ids:  26744\n"
+     ]
+    }
+   ],
+   "source": [
+    "unique_userId = review['userId'].nunique()\n",
+    "\n",
+    "unique_movieId = review['movieId'].nunique()\n",
+    "\n",
+    "print('Total Number of Unique Users Ids: ', unique_userId)\n",
+    "print('Total Number of Unique Movie Ids: ', unique_movieId)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "f04374dc",
+   "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>Labels</th>\n",
+       "      <th>Rating</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5561926</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4291193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2898660</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2200156</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1534824</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1430997</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>883398</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>680732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>279252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>239125</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Labels   Rating\n",
+       "0     4.0  5561926\n",
+       "1     3.0  4291193\n",
+       "2     5.0  2898660\n",
+       "3     3.5  2200156\n",
+       "4     4.5  1534824\n",
+       "5     2.0  1430997\n",
+       "6     2.5   883398\n",
+       "7     1.0   680732\n",
+       "8     1.5   279252\n",
+       "9     0.5   239125"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rating_counts = pd.DataFrame(review['rating'].value_counts()).reset_index()\n",
+    "rating_counts.columns = ['Labels', \"Rating\"]\n",
+    "rating_counts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8b8abb5c",
+   "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>Labels</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Percentage</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5561926</td>\n",
+       "      <td>27.809264</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>4291193</td>\n",
+       "      <td>21.455683</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>2898660</td>\n",
+       "      <td>14.493109</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3.5</td>\n",
+       "      <td>2200156</td>\n",
+       "      <td>11.000635</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.5</td>\n",
+       "      <td>1534824</td>\n",
+       "      <td>7.674019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>1430997</td>\n",
+       "      <td>7.154891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.5</td>\n",
+       "      <td>883398</td>\n",
+       "      <td>4.416932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>680732</td>\n",
+       "      <td>3.403615</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.5</td>\n",
+       "      <td>279252</td>\n",
+       "      <td>1.396242</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>239125</td>\n",
+       "      <td>1.195609</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Labels   Rating  Percentage\n",
+       "0     4.0  5561926   27.809264\n",
+       "1     3.0  4291193   21.455683\n",
+       "2     5.0  2898660   14.493109\n",
+       "3     3.5  2200156   11.000635\n",
+       "4     4.5  1534824    7.674019\n",
+       "5     2.0  1430997    7.154891\n",
+       "6     2.5   883398    4.416932\n",
+       "7     1.0   680732    3.403615\n",
+       "8     1.5   279252    1.396242\n",
+       "9     0.5   239125    1.195609"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rating_counts['Percentage'] = (rating_counts['Rating']/rating_counts['Rating'].sum()*100)\n",
+    "rating_counts.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "528cee3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1500x700 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(15,7))\n",
+    "sns.countplot(review['rating'], ax=ax1)\n",
+    "ax1.set_xlabel('Rating Distribution', fontsize=10)\n",
+    "ax1.set_ylabel('Count', fontsize=10)\n",
+    "\n",
+    "explode = (0.1, 0, 0.1, 0, 0, 0.1, 0, 0.1, 0, 0)\n",
+    "ax2.pie(rating_counts['Rating'], explode=explode, labels=rating_counts.Labels, autopct='%1.2f%%',\n",
+    "       shadow=True, startangle=70)\n",
+    "ax2.axis('equal')\n",
+    "plt.title(\"Rating Ratio\")\n",
+    "plt.legend(rating_counts.Labels, loc=3)\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "97015949",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "import tensorflow as tf\n",
+    "from tensorflow.keras import layers\n",
+    "from tensorflow.keras.models import Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8fd850c0",
+   "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>userId</th>\n",
+       "      <th>movieId</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>recommend</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>20000258</th>\n",
+       "      <td>138493</td>\n",
+       "      <td>68954</td>\n",
+       "      <td>3.974471</td>\n",
+       "      <td>2009-11-13 15:42:00</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20000259</th>\n",
+       "      <td>138493</td>\n",
+       "      <td>69526</td>\n",
+       "      <td>3.974471</td>\n",
+       "      <td>2009-12-03 18:31:48</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20000260</th>\n",
+       "      <td>138493</td>\n",
+       "      <td>69644</td>\n",
+       "      <td>2.474471</td>\n",
+       "      <td>2009-12-07 18:10:57</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20000261</th>\n",
+       "      <td>138493</td>\n",
+       "      <td>70286</td>\n",
+       "      <td>4.474471</td>\n",
+       "      <td>2009-11-13 15:42:24</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20000262</th>\n",
+       "      <td>138493</td>\n",
+       "      <td>71619</td>\n",
+       "      <td>1.974471</td>\n",
+       "      <td>2009-10-17 20:25:36</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          userId  movieId    rating            timestamp  recommend\n",
+       "20000258  138493    68954  3.974471  2009-11-13 15:42:00       True\n",
+       "20000259  138493    69526  3.974471  2009-12-03 18:31:48       True\n",
+       "20000260  138493    69644  2.474471  2009-12-07 18:10:57      False\n",
+       "20000261  138493    70286  4.474471  2009-11-13 15:42:24       True\n",
+       "20000262  138493    71619  1.974471  2009-10-17 20:25:36      False"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We start by creating a binary target variable as pertaining to rating\n",
+    "\n",
+    "actual_mean = review['rating'].mean()\n",
+    "review['rating'] = review['rating'] + (3.0 - actual_mean)\n",
+    "\n",
+    "review['recommend'] = review['rating'] > 3.0\n",
+    "review.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ad9a3fe7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False    10004853\n",
+       "True      9995410\n",
+       "Name: recommend, dtype: int64"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "review['recommend'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d3364e22",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Recommendation Distribution')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(7,7))\n",
+    "colors = ('#f72585', '#4cc9f0')\n",
+    "wp = {'linewidth':2, 'edgecolor': 'black'}\n",
+    "tags = review['recommend'].value_counts()\n",
+    "explode = (0.1, 0.1)\n",
+    "tags.plot(kind='pie', autopct='%1.1f%%', shadow = True, colors = colors,\n",
+    "         startangle=45, wedgeprops= wp, explode = explode, label='')\n",
+    "plt.title('Recommendation Distribution')\n",
+    "                  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "9edce5b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's handle the data imbalance using oversampling (SMOTE)\n",
+    "X = review[['userId', 'movieId']]\n",
+    "y = review['recommend']\n",
+    "smote = SMOTE(random_state=42)\n",
+    "X_sampled, y_sampled = smote.fit_resample(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "c3accab3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's make a Feature Scaling\n",
+    "scaler = StandardScaler()\n",
+    "X_sampled[['userId', 'movieId']] = scaler.fit_transform(X_sampled[['userId', 'movieId']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d2013cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# we proceed by train-testing splitting \n",
+    "X_train, X_test, y_train, y_test = train_test_split(X_sampled, y_sampled, test_size=0.33)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b0ec99f7",
+   "metadata": {},
+   "source": [
+    "### Model Classification and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "05409e2d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/3\n",
+      "83791/83791 [==============================] - 261s 3ms/step - loss: 0.6881 - accuracy: 0.5327 - val_loss: 0.6869 - val_accuracy: 0.5384\n",
+      "Epoch 2/3\n",
+      "83791/83791 [==============================] - 245s 3ms/step - loss: 0.6862 - accuracy: 0.5375 - val_loss: 0.6853 - val_accuracy: 0.5396\n",
+      "Epoch 3/3\n",
+      "83791/83791 [==============================] - 249s 3ms/step - loss: 0.6860 - accuracy: 0.5382 - val_loss: 0.6852 - val_accuracy: 0.5379\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Tensorflow model\n",
+    "input_layer = layers.Input(shape=(2,))\n",
+    "dense_1 = layers.Dense(128, activation='relu')(input_layer)\n",
+    "dense_2 = layers.Dense(64, activation='relu')(dense_1)\n",
+    "output_layer = layers.Dense(1, activation='sigmoid')(dense_2)\n",
+    "tf_model = Model(inputs=input_layer, outputs=output_layer)\n",
+    "\n",
+    "tf_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
+    "tf_model.fit(X_train, y_train, epochs=3, batch_size=128, validation_split=0.2)\n",
+    "tf_preds = tf_model.predict(X_test)\n",
+    "tf_accuracy = accuracy_score(y_test, tf_preds.round())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "17ed7ee7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_model = LogisticRegression(random_state=42)\n",
+    "lr_model.fit(X_train, y_train)\n",
+    "lr_preds = lr_model.predict(X_test)\n",
+    "lr_accuracy = accuracy_score(y_test, lr_preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "0f8df6a8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "knn_model = KNeighborsClassifier(n_neighbors=5)\n",
+    "knn_model.fit(X_train, y_train)\n",
+    "knn_preds = knn_model.predict(X_test)\n",
+    "knn_accuracy = accuracy_score(y_test, knn_preds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2b1fd1f5",
+   "metadata": {},
+   "source": [
+    "### Classification Report for a more comprensive evaluation\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e07e6854",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lr_report = classification_report(y_test, lr_preds)\n",
+    "tf_report = classification_report(y_test, tf_preds.round())\n",
+    "knn_report = classification_report(y_test, knn_preds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "b927f6a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression Accuracy: 0.5052938702626589\n",
+      "TensorFlow Accuracy: 0.5375694189622824\n",
+      "KNN Accuracy: 0.5749172030603936\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Linear Regression Accuracy: {lr_accuracy}')\n",
+    "print(f'TensorFlow Accuracy: {tf_accuracy}')\n",
+    "print(f'KNN Accuracy: {knn_accuracy}')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "08fbbaa4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Linear Regression Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.52      0.14      0.23   3300897\n",
+      "        True       0.50      0.87      0.64   3302306\n",
+      "\n",
+      "    accuracy                           0.51   6603203\n",
+      "   macro avg       0.51      0.51      0.43   6603203\n",
+      "weighted avg       0.51      0.51      0.43   6603203\n",
+      "\n",
+      "TensorFlow Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.52      0.84      0.64   3300897\n",
+      "        True       0.60      0.24      0.34   3302306\n",
+      "\n",
+      "    accuracy                           0.54   6603203\n",
+      "   macro avg       0.56      0.54      0.49   6603203\n",
+      "weighted avg       0.56      0.54      0.49   6603203\n",
+      "\n",
+      "KNN Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "       False       0.52      0.84      0.64   3300897\n",
+      "        True       0.60      0.24      0.34   3302306\n",
+      "\n",
+      "    accuracy                           0.54   6603203\n",
+      "   macro avg       0.56      0.54      0.49   6603203\n",
+      "weighted avg       0.56      0.54      0.49   6603203\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Linear Regression Classification Report')\n",
+    "print(lr_report)\n",
+    "print('TensorFlow Classification Report')\n",
+    "print(tf_report)\n",
+    "print('KNN Classification Report')\n",
+    "print(tf_report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a3c8ebb",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}