This repository contains a comprehensive collection of my second-year Data Science lab assignments implemented using Python and Jupyter Notebook.
💡 This repository demonstrates end-to-end data science workflows from raw data preprocessing to model building, evaluation, and visualization.
- Understand and apply data preprocessing techniques
- Perform statistical analysis and derive meaningful insights
- Build and evaluate machine learning models
- Implement time series forecasting methods
- Visualize data effectively using Python libraries
- Python 🐍
- Jupyter Notebook
- Pandas & NumPy
- Matplotlib & Seaborn
- Scikit-learn
- Statsmodels
📁 Click to expand
Data-Science-Lab-Assignments/
│
├── 01_Data_Wrangling/
├── 02_Data_Wrangling_II/
├── 03_Descriptive_Statistics/
├── 04_Linear_Regression/
├── 05_Logistic_Regression/
├── 06_Naive_Bayes/
├── 07_Time_Series_MA/
├── 08_Auto_Regressive/
├── 09_Moving_Average/
├── 10_ARIMA/
├── 11_Data_Visualization/
│
├── datasets/
└── README.md
- Data collection from platforms like Kaggle
- Data cleaning and preprocessing
- Handling missing values
- Data type conversion and normalization
- Encoding categorical variables
- Academic dataset creation
- Missing value and inconsistency handling
- Outlier detection and treatment
- Data transformation (scaling, normalization, skewness reduction)
- Measures of central tendency (mean, median, mode)
- Measures of variability (standard deviation, variance)
- Grouped statistical analysis
- Analysis using Iris dataset
- 📈 Linear Regression (House Price Prediction)
- 📊 Logistic Regression (Classification)
- 🌸 Naïve Bayes (Iris Dataset)
- Moving Average techniques
- Auto-Regressive (AR) model
- Weighted Moving Average
- ARIMA model implementation
- Trend and seasonality analysis
- Titanic dataset analysis
- Histogram for fare distribution
- Box plots (age vs gender vs survival)
- Insight generation and pattern discovery
- Hands-on experience with real-world datasets
- Strong understanding of data preprocessing techniques
- Ability to build and evaluate ML models
- Knowledge of time series forecasting
- Improved data visualization and storytelling skills
- Clone the repository:
git clone https://github.com/tejasvinifulari5/Data-Science-Lab-Assignments.git- Navigate to the project folder:
cd Data-Science-Lab-Assignments- Open Jupyter Notebook:
jupyter notebook- Add more real-world datasets
- Convert assignments into full-scale projects
- Deploy machine learning models
- Improve visualizations and dashboards
Tejasvini Fulari
If you found this useful, consider giving it a ⭐ on GitHub!
Datasets are sourced from open platforms like Kaggle and built-in Python libraries.