🔍 Machine Learning Lab Repository

Welcome to the Machine Learning Lab repository! This repository includes seven comprehensive lab assignments and a mini project, each aimed at developing practical skills in data preprocessing, exploratory data analysis (EDA), model development, evaluation, and optimization using real-world datasets.

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📁 Lab 1: Titanic Survival Prediction

Objective:
Prepare the Titanic dataset for machine learning and perform predictive analysis.

Tasks:

• Perform basic EDA using head(), tail(), describe(), shape
• Handle missing values and duplicates
• Identify and treat outliers
• Apply data encoding techniques
• Conduct Univariate, Bivariate & Multivariate analysis
• Perform feature scaling
• Split dataset (80:20)

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📁 Lab 2: Used Car Price Prediction

Objective:
Build a regression model to estimate the selling price of used cars.

Tasks:

• Perform EDA
• Apply Linear Regression & Multiple Linear Regression
• Evaluate model using MAE, MSE, RMSE, R² Score
• Apply hyperparameter tuning

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📁 Lab 3: Customer Purchase Prediction

Objective:
Predict if a customer will purchase a product based on behavior and demographics.

Tasks:

• Perform EDA
• Apply Logistic Regression, Decision Tree, and KNN
• Evaluate using Accuracy, Precision, Recall, and F1-Score
• Apply hyperparameter tuning

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📁 Lab 4: Dimensionality Reduction with PCA

Objective:
Classify hand-written digits using logistic regression after reducing dimensions.

Tasks:

• Apply Principal Component Analysis (PCA)
• Train model using Logistic Regression
• Evaluate classification performance

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📁 Lab 5: Loan Default Prediction Using Ensemble Methods

Objective:
Predict loan default risk using ensemble techniques.

Tasks:

• Perform EDA
• Apply Random Forest and AdaBoost classifiers
• Evaluate using Accuracy, Precision, Recall, and F1-Score
• Develop an ANN model for comparison
• Apply hyperparameter tuning

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📁 Lab 6: Heart Attack Risk Prediction

Objective:
Predict whether a person is at risk of a heart attack.

Tasks:

• Perform EDA
• Apply Naive Bayes and Support Vector Machine (SVM)
• Evaluate using Accuracy, Precision, Recall, and F1-Score
• Apply hyperparameter tuning

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📁 Lab 7: Customer Segmentation with K-Means

Objective:
Help a shopkeeper segment customers for targeted marketing.

Tasks:

• Perform EDA
• Apply K-Means Clustering algorithm
• Visualize clusters and provide business insights

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📁 Mini Project: Classification Model Comparison

Objective:
Build and compare multiple classification models on a chosen dataset.

Tasks:

• Choose dataset of your interest
• Perform detailed EDA
• Apply the following algorithms:
  • Logistic Regression (LR)
  • Naive Bayes (NB)
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Decision Tree (DT)
  • Random Forest (RF)
  • AdaBoost
• Evaluate using classification metrics
• Compare all models and visualize results

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🛠 Technologies Used

• Python
• Pandas, NumPy, Matplotlib, Seaborn
• Scikit-learn
• TensorFlow / Keras (for ANN)
• Jupyter Notebooks

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📌 How to Use

1. Clone this repository:

   git clone https://github.com/yourusername/machine-learning-labs.git