Data Science Projects: Comprehensive Solutions Repository

Welcome to the repository showcasing a collection of data science projects. This repository contains solutions for various tasks, demonstrating key aspects of data science, including data preprocessing, model building, evaluation, and prediction.

Table of Contents

1. Task 1: Titanic Survival Prediction
2. Task 2: Movie Rating Prediction with Python
3. Task 3: Iris Flower Classification
4. Task 4: Sales Prediction Using Python
5. Task 5: Credit Card Fraud Detection

---

Task 1: Titanic Survival Prediction

In this project, survival chances of passengers aboard the Titanic were predicted based on features such as age, class, and sex.

Key Concepts:

• Data Cleaning and Preprocessing
• Feature Engineering
• Logistic Regression Model
• Accuracy Evaluation

Key Libraries Used:

• pandas, numpy, matplotlib, seaborn, scikit-learn

---

Task 2: Movie Rating Prediction with Python

This project involved predicting movie ratings based on user preferences using machine learning models such as Linear Regression and Decision Trees.

Key Concepts:

• Data Cleaning, Preprocessing, and Formatting
• Linear Regression and Random Forest Regressor
• Evaluation Metrics: MSE

Key Libraries Used:

• pandas, numpy, scikit-learn, matplotlib

---

Task 3: Iris Flower Classification

I classified Iris flower species based on sepal and petal dimensions using a variety of machine learning classifiers, including K-Nearest Neighbors and Random Forest.

Key Concepts:

• Logistic Regression Model
• Data Visualization
• Model Evaluation: Accuracy, Classification Report, Confusion Matrix

Key Libraries Used:

• pandas, numpy, scikit-learn, seaborn, matplotlib

---

Task 4: Sales Prediction Using Python

This task involves predicting sales based on marketing data such as TV, Radio, and Newspaper spending. The model predicts sales using Linear Regression and Random Forest Regression.

Key Concepts:

• Regression Analysis
• Model Tuning
• Feature Engineering

Key Libraries Used:

• pandas, numpy, matplotlib, seaborn, scikit-learn

---

Task 5: Credit Card Fraud Detection

In this project, a machine learning model was built to predict fraudulent transactions based on historical transaction data.

Key Concepts:

• Anomaly Detection
• Model Evaluation
• Handling Imbalanced Data

Key Libraries Used:

• pandas, numpy, scikit-learn, imbalanced-learn

---

Requirements

To run the code in this repository, you'll need to install the following Python packages:

pip install pandas numpy matplotlib seaborn scikit-learn imbalanced-learn

Conclusion

These projects demonstrate various aspects of data science, from classification to regression and anomaly detection. Each project showcases skills in building and evaluating machine learning models.