1+ {
2+ "nbformat" : 4 ,
3+ "nbformat_minor" : 0 ,
4+ "metadata" : {
5+ "colab" : {
6+ "provenance" : []
7+ },
8+ "kernelspec" : {
9+ "name" : " python3"
10+ "display_name" : " Python 3"
11+ },
12+ "language_info" : {
13+ "name" : " python"
14+ }
15+ },
16+ "cells" : [
17+ {
18+ "cell_type" : " code"
19+ "execution_count" : 2 ,
20+ "metadata" : {
21+ "colab" : {
22+ "base_uri" : " https://localhost:8080/"
23+ },
24+ "id" : " K6ZifZM98GkT"
25+ "outputId" : " efd22c1c-6ba0-4d1e-94a6-a666f260623b"
26+ },
27+ "outputs" : [
28+ {
29+ "output_type" : " stream"
30+ "name" : " stdout"
31+ "text" : [
32+ " Predicted values: [0 1 0 ... 1 1 1]\n "
33+ " Actual values: [0 1 0 ... 1 1 1]\n "
34+ " Accuracy of the model: 87.39%\n "
35+ " Confusion Matrix:\n "
36+ " [[ 39912 19954]\n "
37+ " [ 5261 134873]]\n "
38+ ]
39+ }
40+ ],
41+ "source" : [
42+ " import numpy as np\n "
43+ " import pandas as pd\n "
44+ " from sklearn.model_selection import train_test_split\n "
45+ " from sklearn.preprocessing import StandardScaler\n "
46+ " from sklearn.linear_model import LogisticRegression\n "
47+ " from sklearn.metrics import accuracy_score, confusion_matrix\n "
48+ " \n "
49+ " df = pd.read_csv('large_customer_data.csv')\n "
50+ " \n "
51+ " X = df[['Age', 'EstimatedSalary']]\n "
52+ " y = df['Purchased']\n "
53+ " \n "
54+ " X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n "
55+ " \n "
56+ " scaler = StandardScaler()\n "
57+ " X_train_scaled = scaler.fit_transform(X_train)\n "
58+ " X_test_scaled = scaler.transform(X_test)\n "
59+ " \n "
60+ " model = LogisticRegression()\n "
61+ " model.fit(X_train_scaled, y_train)\n "
62+ " \n "
63+ " y_pred = model.predict(X_test_scaled)\n "
64+ " \n "
65+ " accuracy = accuracy_score(y_test, y_pred)\n "
66+ " conf_matrix = confusion_matrix(y_test, y_pred)\n "
67+ " \n "
68+ " print(f\" Predicted values: {y_pred}\" )\n "
69+ " print(f\" Actual values: {y_test.values}\" )\n "
70+ " print(f\" Accuracy of the model: {accuracy * 100:.2f}%\" )\n "
71+ " print(f\" Confusion Matrix:\\ n{conf_matrix}\" )\n "
72+ ]
73+ },
74+ {
75+ "cell_type" : " code"
76+ "source" : [],
77+ "metadata" : {
78+ "id" : " BTu91qrS8K9O"
79+ },
80+ "execution_count" : null ,
81+ "outputs" : []
82+ }
83+ ]
84+ }
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