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