Machine Learning Foundations 🚀

A beginner-to-intermediate repository focused on:

• Machine Learning workflows
• Exploratory Data Analysis (EDA)
• Data Cleaning
• Feature Engineering
• Statistical Testing
• Regression Models

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Topics Covered

Data Preprocessing

• Handling missing values
• Duplicate removal
• Outlier treatment
• Label Encoding

Exploratory Data Analysis

• Boxplots
• Regression plots
• Distribution analysis

Machine Learning

• Linear Regression
• Model evaluation
• R² score analysis

Statistics

• Hypothesis Testing
• One Sample T-Test
• Two Proportion Z-Test
• P-value interpretation

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Projects Included

1. Linear Regression Pipeline

Predicting continuous target variables using preprocessing and regression modeling.

Concepts Used

• Null handling
• Outlier detection
• Label Encoding
• Train-Test Split
• Linear Regression
• Regression Evaluation

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2. Purchase Analysis & Hypothesis Testing

Business-oriented statistical analysis on customer purchase behavior.

Statistical Tests

• One Sample T-Test
• Two Proportion Z-Test

Business Questions Solved

• Are male customers aged 18–25 spending ₹10,000 on average?
• Do women contribute more than 35% of purchases above ₹10,000?
• Is spending behavior similar between age groups?

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Tools & Libraries

• Python
• NumPy
• Pandas
• Matplotlib
• Seaborn
• Scikit-learn
• SciPy
• Statsmodels

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Learning Outcome

This repository demonstrates:

• End-to-end ML workflow
• Data preprocessing
• Statistical analysis
• Business problem solving
• Model evaluation techniques