DataScienceAndBigDataAnalytics/Codes/Code-A5 (Data Analytics-2).md

A5 - Data Analytics-2

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Pre-requisites

• Install required libraries: pandas & scikit-learn

pip install pandas
pip install -U scikit-learn

• Save the dataset Social_Network_Ads.csv in the same directory as this Jupyter notebook.

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Code blocks

1. Import libraries:

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:

df = pd.read_csv("Social_Network_Ads.csv")
df["Gender"].replace({"Male":0,"Female":1}, inplace=True)
df

3. Print columns of the DataFrame:

df.columns

4. Defining the feature set (X) and the target variable (y):

x = df[['User ID', 'Gender', 'Age', 'EstimatedSalary']]
y = df[['Purchased']]

5. Splitting the dataset into training and testing sets (75% training, 25% testing):

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:

model = LogisticRegression()
model.fit(x_train,y_train)

7. Making and displaying predictions on the test set using the trained model:

y_pred = model.predict(x_test)
y_pred

8. Evaluating the model's performance on the training set:

model.score(x_train,y_train)

9. Evaluating the model's performance on the entire dataset:

model.score(x,y)

10. Generating and displaying the confusion matrix to evaluate the model's predictions:

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):

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:

a = accuracy_score(y_test,y_pred)
a

13. Calculating and displaying the error rate (1 - accuracy):

e = 1 - a
e

14. Calculating the precision score of the model:

precision_score(y_test,y_pred)

15. Calculating the recall score of the model:

recall_score(y_test,y_pred)

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References

1. Jupyter notebook
2. Dataset source

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