Lab_9_Logestic_Regression_Real_example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from math import exp\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>User ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>EstimatedSalary</th>\n",
       "      <th>Purchased</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15624510</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15810944</td>\n",
       "      <td>Male</td>\n",
       "      <td>35</td>\n",
       "      <td>20000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15668575</td>\n",
       "      <td>Female</td>\n",
       "      <td>26</td>\n",
       "      <td>43000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15603246</td>\n",
       "      <td>Female</td>\n",
       "      <td>27</td>\n",
       "      <td>57000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15804002</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>76000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    User ID  Gender  Age  EstimatedSalary  Purchased\n",
       "0  15624510    Male   19            19000          0\n",
       "1  15810944    Male   35            20000          0\n",
       "2  15668575  Female   26            43000          0\n",
       "3  15603246  Female   27            57000          0\n",
       "4  15804002    Male   19            76000          0"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"datasets_1846_3197_Social_Network_Ads.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15b7f0df3a0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data['Age'], data['Purchased'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(data['Age'], data['Purchased'], test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(400, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(280,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sigmoid Funtion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x15b7f12e430>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-54, 54, 50) \n",
    "z = 1/(1 + np.exp(-x))\n",
    "plt.plot(x, z) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121    37\n",
       "389    48\n",
       "189    28\n",
       "107    27\n",
       "229    42\n",
       "       ..\n",
       "164    33\n",
       "19     48\n",
       "208    40\n",
       "167    35\n",
       "293    37\n",
       "Name: Age, Length: 280, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def normalize(X):\n",
    "    return X - X.mean()"
   ]
  },
  {
   "attachments": {
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   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture2.jpg](attachment:Picture2.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121     0.15\n",
       " 389    11.15\n",
       " 189     8.85\n",
       " 107     9.85\n",
       " 229     5.15\n",
       "        ...  \n",
       " 164     3.85\n",
       " 19     11.15\n",
       " 208     3.15\n",
       " 167     1.85\n",
       " 293     0.15\n",
       " Name: Age, Length: 280, dtype: float64,\n",
       " 121    37\n",
       " 389    48\n",
       " 189    28\n",
       " 107    27\n",
       " 229    42\n",
       "        ..\n",
       " 164    33\n",
       " 19     48\n",
       " 208    40\n",
       " 167    35\n",
       " 293    37\n",
       " Name: Age, Length: 280, dtype: int64)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(X_train-X_train.mean()),X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(X, b0, b1):\n",
    "    return np.array([1 / (1 + exp(-b0 -b1*x)) for x in X])"
   ]
  },
  {
   "attachments": {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture3.jpg](attachment:Picture3.jpg)"
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Picture4.jpg](attachment:Picture4.jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_regression(X, Y):\n",
    "    X = normalize(X)\n",
    "    b0 = 0\n",
    "    b1 = 0\n",
    "    L = 0.001\n",
    "    epochs = 500\n",
    "    for epoch in range(epochs):\n",
    "        y_pred = predict(X, b0, b1)\n",
    "        D_b0 = (-2/len(X)) * sum((Y - y_pred) * y_pred * (1 - y_pred))  # Derivative of loss wrt b0\n",
    "        D_b1 = (-2/len(X)) * sum(X * (Y - y_pred) * y_pred * (1 - y_pred))  # Derivative of loss wrt b1\n",
    "        # Update b0 and b1\n",
    "        b0 = b0 - L * D_b0\n",
    "        b1 = b1 - L * D_b1\n",
    "    return b0, b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "b0, b1 = logistic_regression(X_train, y_train)\n",
    "\n",
    "# Making predictions\n",
    "X_test_norm = normalize(X_test)\n",
    "y_pred = predict(X_test_norm, b0, b1)\n",
    "y_pred = [1 if p >= 0.5 else 0 for p in y_pred]\n",
    "#y_pred1 = [1 if p >= 0.1 else 0 for p in y_pred]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.count_nonzero(y_pred),np.count_nonzero(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(y_pred)):\n",
    "   (y_pred[i] == y_test.iloc[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y = y_test.iloc[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "205    0\n",
       "113    0\n",
       "327    0\n",
       "298    0\n",
       "131    0\n",
       "      ..\n",
       "0      0\n",
       "235    1\n",
       "245    1\n",
       "186    0\n",
       "49     0\n",
       "Name: Purchased, Length: 120, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.count_nonzero(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy = 0.775\n"
     ]
    }
   ],
   "source": [
    "plt.clf()\n",
    "plt.scatter(X_test, y_test)\n",
    "plt.scatter(X_test, y_pred, c=\"red\")\n",
    "plt.show()\n",
    "\n",
    "# The accuracy\n",
    "accuracy = 0\n",
    "for i in range(len(y_pred)):\n",
    "    if y_pred[i] == y_test.iloc[i]:\n",
    "        accuracy += 1\n",
    "print(f\"Accuracy = {accuracy / len(y_pred)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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