From 97ed1414ea6f46b194ae25658f9cee49c240b016 Mon Sep 17 00:00:00 2001
From: Kshitij <notkshitij@git.kska.io>
Date: Fri, 28 Mar 2025 11:07:20 +0530
Subject: [PATCH] Added code for A5 (data analytics 2), i.e. logistic
 regression.

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 Codes/Code-A5 (Data Analytics-2).md | 130 ++++++++++++++++++++++++++++
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+# A5 - Data Analytics-2
+
+---
+
+## Pre-requisites:
+
+- Install required libraries: `pandas` & `scikit-learn`
+
+```shell
+pip install pandas
+pip install -U scikit-learn
+```
+
+- Save the dataset [Social_Network_Ads.csv](https://git.kska.io/sppu-te-comp-content/DataScienceAndBigDataAnalytics/src/branch/main/Datasets/Social_Network_Ads.csv) in the same directory as this Jupyter notebook.
+
+---
+
+## Code blocks:
+
+1. Import libraries:
+
+```shell
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score
+```
+
+2. Load the dataset from a CSV file into a pandas DataFrame:
+
+```shell
+df = pd.read_csv("Social_Network_Ads.csv")
+df["Gender"].replace({"Male":0,"Female":1}, inplace=True)
+df
+```
+
+3. Print columns of the DataFrame:
+
+```shell
+df.columns
+```
+
+4. Defining the feature set (X) and the target variable (y):
+
+```shell
+x = df[['User ID', 'Gender', 'Age', 'EstimatedSalary']]
+y = df[['Purchased']]
+```
+
+5. Splitting the dataset into training and testing sets (75% training, 25% testing):
+
+```shell
+x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.25,random_state=29)
+```
+
+6. Creating an instance of the Logistic Regression model & fitting the model to the training data:
+
+```shell
+model = LogisticRegression()
+model.fit(x_train,y_train)
+```
+
+7. Making and displaying predictions on the test set using the trained model:
+
+```shell
+y_pred = model.predict(x_test)
+y_pred
+```
+
+8. Evaluating the model's performance on the training set:
+
+```shell
+model.score(x_train,y_train)
+```
+
+9. Evaluating the model's performance on the entire dataset:
+
+```shell
+model.score(x,y)
+```
+
+10. Generating and displaying the confusion matrix to evaluate the model's predictions:
+
+```shell
+cm = confusion_matrix(y_test,y_pred)
+cm
+```
+
+11. Unpacking and printing the confusion matrix into true negatives (tn), false positives (fp), false negatives (fn), and true positives (tp):
+
+```shell
+tn, fp, fn, tp = confusion_matrix(y_test,y_pred).ravel()
+print(tn,fp,fn,tp)
+```
+
+12. Calculating and displaying the accuracy score of the model on the test set:
+
+```shell
+a = accuracy_score(y_test,y_pred)
+a
+```
+
+13. Calculating and displaying the error rate (1 - accuracy):
+
+```shell
+e = 1 - a
+e
+```
+
+14. Calculating the precision score of the model:
+
+```shell
+precision_score(y_test,y_pred)
+```
+
+15. Calculating the recall score of the model:
+
+```shell
+recall_score(y_test,y_pred)
+```
+
+---
+
+## References
+
+1. [Jupyter notebook](https://github.com/ganimtron-10/SPPU-2019-TE-DSBDA-Lab/blob/master/Group-A/Q5.ipynb)
+2. [Dataset source](https://www.kaggle.com/datasets/akram24/social-network-ads)
+
+---