Internship Data Science

A collection of machine learning and data science notebooks developed during an internship training program. Covers a wide range of foundational and intermediate ML algorithms, applied to real datasets with hands-on implementations in Python.

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📁 Repository Structure

Notebook	Topic
Linear regression_1 (1).ipynb	Linear Regression
Logistic Regression.ipynb	Logistic Regression
Decision Trees.ipynb	Decision Trees
RandomForest.ipynb	Random Forest
K-Nearest Neighbors.ipynb	K-Nearest Neighbors (KNN)
K-Means Clustering - Few Examples.ipynb	K-Means Clustering
Linear Discriminant Analysis.ipynb	Linear Discriminant Analysis (LDA)
Principle Component Analysis - Few Examples.ipynb	Principal Component Analysis (PCA)
Face recognition_Data CollectionCode.ipynb	Face Recognition (Data Collection)
COVID-19 visualizations.ipynb	COVID-19 Data Visualization
DMG-1 Assignment	Assignment / Exercise

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📊 Topics Covered

Supervised Learning

• Linear Regression — predicting continuous outcomes
• Logistic Regression — binary classification
• Decision Trees — interpretable rule-based models
• Random Forest — ensemble learning for improved accuracy
• K-Nearest Neighbors — distance-based classification

Unsupervised Learning

• K-Means Clustering — grouping unlabeled data
• Principal Component Analysis (PCA) — dimensionality reduction

Dimensionality Reduction & Discrimination

• Linear Discriminant Analysis (LDA) — class-separating projections

Computer Vision

• Face Recognition — data collection pipeline using OpenCV

Data Visualization

• COVID-19 Visualizations — trend analysis and charting of pandemic data

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🛠️ Technologies & Libraries

• Python 3.x
• Jupyter Notebook
• pandas, numpy
• scikit-learn
• matplotlib, seaborn
• OpenCV (for face recognition)

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🚀 Getting Started

1. Clone the repository:

   git clone https://github.com/janmejoykar1807/Internship_Data_Science.git

2. Install dependencies:

   pip install pandas numpy scikit-learn matplotlib seaborn opencv-python jupyter

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open any .ipynb file to explore the topic.

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👤 Author

Janmejoy Kar Data Science learner — applying Python, R, and SQL for data analysis and predictive modeling. GitHub Profile