🚀 Data Science ( Analytics) & Machine Learning

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📊 Project Overview

This repository is a comprehensive collection of Data Science and Machine Learning projects.

It demonstrates:

✨ Data Analysis
🤖 Machine Learning
📊 Statistical Testing (ANOVA)
📈 Advanced Visualization
🎬 Animated Graphs
🌍 Real-world datasets

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📁 Repository Structure

📦 Project Root

• 📂 codes → Jupyter notebooks (ML, EDA, visualization)
• 📂 data → Real-world datasets (CSV, Excel)
• 📜 README.md

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🔥 Featured Projects

🚢 Titanic Survival Analysis

✔ ANOVA testing
✔ Survival prediction
✔ Confusion matrix + ROC curve
✔ Advanced visualization

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📱 Social Media Analytics

✔ Engagement analysis
✔ Trend detection
✔ Histogram & pie charts

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🏏 IPL Data Analysis

✔ Player performance
✔ Career graph
✔ Team prediction model

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🎬 Netflix Series Analysis

✔ Monthly trending
✔ Genre popularity
✔ Viewer insights

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🌍 Earthquake Analysis

✔ Magnitude distribution
✔ Time trend analysis
✔ Geographic insights

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📈 Economic Growth Prediction

✔ Future trend modeling
✔ Dataset generation
✔ Visualization forecasting

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🤖 Machine Learning Models

✔ Logistic Regression
✔ Linear Regression
✔ Classification & Prediction
✔ Model evaluation

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🎨 Visualization Beyond Basics

• Pairplots
• Heatmaps
• Violin plots
• KDE plots
• 3D Visualizations
• Animated graphs

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Dataset loaded using:

import seaborn as sns
df = sns.load_dataset("titanic")

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📈 Analysis Performed

1️⃣ Data Cleaning

• Removed missing values
• Selected relevant columns
• Prepared dataset for statistical testing

2️⃣ Data Visualization

The project includes multiple visualizations:

• 📊 Bar plots
• 📉 Histograms
• 📦 Boxplots
• 🎬 Animated survival visualization

Example:

sns.barplot(x="class", y="survived", hue="sex", data=df)

3️⃣ Statistical Analysis (Two-Way ANOVA) This analysis tests:

• Effect of Gender
• Effect of Passenger Class
• Interaction between Gender × Class

Example model:

from statsmodels.formula.api import ols

model = ols('survived ~ C(sex) * C(Q("class"))', data=df).fit()

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🎬 Animated Visualization

This project also includes animated plots using Matplotlib.

import matplotlib.animation as animation

Animated plots dynamically display survival rates across classes.

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⚙️ Tech Stack

Python | Pandas | NumPy | Seaborn | Matplotlib | Plotly | Scikit-learn

⚙️ Technologies Used

• Python
• Pandas
• Seaborn
• Matplotlib
• Statsmodels
• Jupyter Notebook

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📷 File Structure

├── 📁 .ipynb_checkpoints
│   ├── 📄 accident_predict-checkpoint.ipynb
│   ├── 📄 gender-checkpoint.ipynb
│   ├── 📄 ios_android-checkpoint.ipynb
│   ├── 📄 kolkata-checkpoint.ipynb
│   ├── 📄 mock_test-checkpoint.ipynb
│   ├── 📄 testing-checkpoint.ipynb
│   └── 📄 train-checkpoint.ipynb
├── 📁 codes
│   ├── 📁 .ipynb_checkpoints
│   │   ├── 📄 advanced.ipynb
│   │   ├── 📄 climate.ipynb
│   │   ├── 📄 earth_quake.ipynb
│   │   ├── 📄 phone-pay_razar_paypal-checkpoint.ipynb
│   │   ├── 📄 test-checkpoint.ipynb
│   │   ├── 📄 titanic-checkpoint.ipynb
│   │   ├── 📄 train-checkpoint.ipynb
│   │   └── 📄 visual.ipynb
│   ├── 📄 JIS.ipynb
│   ├── 📄 accident_predict.ipynb
│   ├── 📄 adavance_pd.ipynb
│   ├── 📄 assigment1.ipynb
│   ├── 📄 breast_cancer.ipynb
│   ├── 📄 earth_quake_2.ipynb
│   ├── 📄 ecomic_gwrth.ipynb
│   ├── 📄 finalproject.ipynb
│   ├── 📄 gender.ipynb
│   ├── 📄 ios_android.ipynb
│   ├── 📄 ipl.ipynb
│   ├── 📄 jis_university.db
│   ├── 📄 jis_university_students.xlsx
│   ├── 📄 kolkata.ipynb
│   ├── 📄 match.ipynb
│   ├── 📄 mock_test.ipynb
│   ├── 📄 netflix.ipynb
│   ├── 📄 new_titanic.ipynb
│   ├── 📄 personl_data.ipynb
│   ├── 📄 phone-pay_razar_paypal.ipynb
│   ├── 📄 socia1l.ipynb
│   ├── 📄 social.ipynb
│   ├── 📄 social_media_usage.xlsx
│   ├── 📄 social_use.ipynb
│   ├── 📄 student_fruit.ipynb
│   ├── 📄 testing.ipynb
│   ├── 📄 testing3.ipynb
│   ├── 📄 titanic.ipynb
│   ├── 📄 titanic2.ipynb
│   ├── 📄 titanic3.ipynb
│   ├── 📄 titanic4ANOVA.ipynb
│   └── 📄 train.ipynb
├── 📁 data
│   ├──  ( all data of .ipynb files )
└── 📝 README.md

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🚀 How to Run the Project

Clone the repository:

git clone https://github.com/yourusername/titanic-analysis.git

Install required libraries

pip install pandas seaborn matplotlib statsmodels

Run Jupyter Notebook

jupyter notebook

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👨‍💻 Author

Amit Paul

💻 Data Science | Python | Machine Learning

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⭐ Support

If you like this project:

• ⭐ Star the repository
• 🍴 Fork the project
• 🚀 Share it with others