GRIP-TheSparksFoundation

Author: Muhammad Sheryar Adil

Data Science

GRIP October'23

Data Analysis Project

This repository contains three tasks involving data analysis and machine learning using Python. The tasks are:

1. Linear Regression Analysis on Student Scores Data
2. K-Means Clustering on Iris Dataset
3. Exploratory Data Analysis on Retail Dataset

Task 1: Linear Regression Analysis on Student Scores Data

Objective

The objective of this task is to predict the scores of students based on the number of hours they studied using linear regression.

Steps

1. Import Libraries:
   • Import necessary libraries such as pandas, numpy, matplotlib, and sklearn.
2. Load Data:
   • Load the dataset from a URL and display the first 15 rows.
3. Data Visualization:
   • Plot the data to visualize the relationship between hours studied and scores obtained.
4. Data Preparation:
   • Split the data into features (hours) and target (scores).
5. Train-Test Split:
   • Split the data into training and testing sets.
6. Model Training:
   • Train a linear regression model on the training data.
7. Model Visualization:
   • Plot the regression line with the data points.
8. Prediction:
   • Make predictions on the test data.
9. Evaluation:
   • Compare the actual and predicted scores.
   • Calculate and display the Mean Absolute Error (MAE).

Output

• A scatter plot of hours vs. scores.

- A line plot of the regression line.

- A comparison of actual and predicted scores. - Mean Absolute Error (MAE) value.

Libraries Used

• pandas
• numpy
• matplotlib
• sklearn

Task 2: K-Means Clustering on Iris Dataset

Objective

The objective of this task is to perform clustering on the Iris dataset to identify different species of Iris flowers.

Steps

1. Import Libraries:
   • Import necessary libraries such as pandas, numpy, matplotlib, and sklearn.
2. Load Data:
   • Load the Iris dataset and display the first 5 rows.
3. Data Preparation:
   • Extract the feature variables from the dataset.
4. Elbow Method:
   • Use the Elbow Method to determine the optimal number of clusters.
5. Model Training:
   • Train a K-Means clustering model with the optimal number of clusters.
6. Cluster Visualization:
   • Visualize the clusters and their centroids.

Output

• A plot showing the Elbow Method to determine the optimal number of clusters.

• A scatter plot visualizing the clusters and centroids.

Libraries Used

• pandas
• numpy
• matplotlib
• sklearn

Exploratory Data Analysis - Retail Dataset

Objective

This project involves performing an exploratory data analysis (EDA) on a retail dataset named 'SampleSuperstore.' The analysis aims to understand the business's overall performance, identify profitable and loss-making segments, and offer insights for strategic decision-making.

Steps

1. Import Libraries:
   • Import necessary libraries such as pandas, numpy, matplotlib, and seaborn.
2. Load Data:
   • Load the dataset and display the first 5 rows.
3. Data Overview:
   • Display summary statistics and check for missing values.
4. Data Cleaning:
   • Remove duplicate entries from the dataset.
5. Overall Profit/Loss Analysis:
   • Calculate and display the overall profit/loss of the business.
6. Category-wise Analysis:
   • Analyze profit/loss and sales by different categories.
7. Region-wise Analysis:
   • Analyze profit/loss and sales by different regions.
8. Sub-Category Analysis:
   • Analyze profit/loss by different sub-categories.
9. Discount Analysis:
   • Analyze the impact of discount on profit/loss.
10. Shipping Mode Analysis:
    • Analyze sales and profit by different shipping modes.
11. Segment Analysis:
    • Analyze sales and profit by different customer segments.
12. State-wise Analysis:
    • Analyze sales and profit by different states.
13. Recommendations:
    • Provide recommendations based on the analysis.

Features of the Analysis

1. Overall Profit/Loss: Calculates the total profit or loss for the business.
2. Profit/Loss by Category: Analyzes profit and loss across different product categories.
3. Profit/Loss by Region: Breaks down profit and loss by geographical region.
4. Profit/Loss by Sub-Category: Examines profit and loss at the sub-category level.
5. Sales by Category: Visualizes sales distribution across categories.
6. Sales by Region: Provides a regional sales breakdown.
7. Profit/Loss by Discount: Investigates the impact of discounts on profit and loss.
8. Profit & Sales by Ship Mode: Analyzes profit and sales based on different shipping methods.
9. Sales & Profit by Segment: Studies the profit and sales across customer segments.
10. Sales & Profit by State: Looks at profit and sales performance at the state level.

Data Analysis

The analysis provides several key insights:

• Overall Profit/Loss: The business has a total profit of $286,241.42.

• Category and Region Performance: Some categories and regions perform better than others. For instance, office supplies generally show positive profits, while furniture and technology vary.

• Impact of Discounts: Higher discounts generally lead to lower profits, indicating the need for optimized discount strategies.

• Shipping and Segmentation Insights: The shipping method and customer segment analysis suggest opportunities to optimize shipping costs and target profitable customer segments.

These insights can be used to make data-driven decisions to improve business profitability and efficiency.

Conclusion

This repository provides a comprehensive analysis of different datasets using linear regression, clustering, and exploratory data analysis techniques. The insights and visualizations derived from these tasks can help in making informed business decisions.