This repository contains a collection of Python notebooks covering fundamental Machine Learning, Statistics, and Data Science concepts. The notebooks demonstrate data preprocessing, exploratory data analysis, statistical testing, predictive modeling, clustering, regression, and ensemble learning using real-world datasets.
- Basic Statistics
- Probability
- Confidence Intervals
- Hypothesis Testing
- Chi-Square Test
- ANOVA
- Exploratory Data Analysis (EDA)
- Data Visualization
- Data Cleaning
- Feature Analysis
- Multiple Linear Regression
- Decision Trees
- Random Forest
- Bagging
- Boosting
- Stacking
- DBSCAN Clustering
- Association Rule Mining
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- SciPy
- Jupyter Notebook
Machine-Learning-Practice/
│
├── Basic Statistics
├── Probability
├── Hypothesis Testing
├── ANOVA
├── Confidence Interval
├── Exploratory Data Analysis
├── Linear Regression
├── Decision Trees
├── Ensemble Learning
├── DBSCAN Clustering
├── Association Rules
└── README.md
This repository demonstrates practical implementation of:
- Data preprocessing
- Statistical analysis
- Data visualization
- Regression algorithms
- Classification algorithms
- Clustering techniques
- Ensemble learning methods
- Model evaluation
- Data Cleaning
- Feature Engineering
- Exploratory Data Analysis
- Statistical Testing
- Predictive Modeling
- Machine Learning
- Python Programming
- Data Visualization
Install the required Python libraries:
pip install pandas numpy matplotlib seaborn scikit-learn scipy jupyter- Clone the repository.
git clone https://github.com/nousheentabassum/machine-learning-practice.git-
Open any notebook using Jupyter Notebook or Google Colab.
-
Run the notebook cells sequentially.
Nousheen Tabassum