🧪 Data Science & Statistic Modelling Laboratory

Complete Collection of 10 Experiments (DS & ML Lab)

This repository contains all 10 experiments performed as part of the Data Science & Machine Learning Laboratory course.
Each experiment includes:

• Aim
• Theory
• Step-by-step procedure
• Python program
• Result & conclusion
• Well-structured README for easy understanding

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📌 Table of Contents

Experiment No.	Title
01	Data Collection & Creation of DataFrame using Pandas
02	Create a Simple Dashboard using Streamlit, Pandas, Plotly
03	Data Discovery, Profiling & Data Dictionary
04	Data Cleaning using Pandas & NumPy
05	Data Transformation (Scaling, Encoding, Normalization)
06	Outlier Detection & Treatment (IQR, Z-Score, Winsorization)
07	Data Integration & Dimensionality Reduction (PCA)
08	Feature Selection (SelectKBest, RFE, Pipeline)
09	Full Exploratory Data Analysis (Titanic Case Study)
10	Point Estimates & Confidence Intervals (t, z, Bootstrap)

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📁 Repository Structure

📦 DS-ML-Lab-Experiments
┣ 📂 Experiment_01
┣ 📂 Experiment_02
┣ 📂 Experiment_03
┣ 📂 Experiment_04
┣ 📂 Experiment_05
┣ 📂 Experiment_06
┣ 📂 Experiment_07
┣ 📂 Experiment_08
┣ 📂 Experiment_09
┗ 📂 Experiment_10

Each folder contains:

• README.md (Theory + Steps + Code + Result + Conclusion)
• .py or .ipynb files
• Any additional data files (CSV, etc.)

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🧠 About the Lab

The goal of this lab work is to build core skills in:

• Data preprocessing
• Data cleaning & transformation
• Feature engineering
• Data visualization
• Exploratory data analysis
• Dashboard building
• Statistical inference
• Dimensionality reduction (PCA)
• Feature selection

These experiments prepare students for practical machine learning & industry-level data workflows.

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🚀 Technologies Used

• Python 3.x
• Pandas
• NumPy
• Matplotlib & Seaborn
• Scikit-learn
• Streamlit
• Plotly
• Statsmodels

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🔥 Highlights

✔ Clean, well-documented code
✔ Stepwise procedures as per university guidelines
✔ Visualizations included wherever required
✔ Industry-style EDA & preprocessing workflows
✔ Ready to run on Jupyter Notebook or VS Code

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📥 How to Use This Repository

1. Clone the repository

   git clone https://github.com/Kaliya-Network/DS-SM.git

2. Open any experiment folder
3. Run the .ipynb or .py file
4. Refer to README.md inside each experiment for explanations

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📝 Author

Kaliya-Network B.Tech CSE • Assam down Town University GitHub: https://github.com/Kaliya-Network/

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⭐ Support

If you found this repository helpful, please star ⭐ it on GitHub!