🌟 Elevvo Internship Program — Machine Learning Track

Welcome to my Elevvo Internship Program repository!
This repository documents my journey, tasks, and completed projects during the Elevvo internship focused on Machine Learning and AI-driven problem-solving.

Each notebook demonstrates a different real-world use case — from predictive modeling and clustering to computer vision and recommendation systems.

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🧭 Overview

The Elevvo Internship Program allowed me to apply data science and ML concepts on various datasets.
It strengthened my understanding of the end-to-end ML workflow — data preprocessing, model training, hyperparameter tuning, and evaluation.

Participants were expected to complete:

• ✅ 4+ tasks for a 1-month internship

This repository contains all completed core tasks.

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🧠 Learning Objectives

• Apply supervised and unsupervised learning techniques on diverse datasets.
• Explore data preprocessing, feature engineering, and model evaluation.
• Build real-world machine learning projects using Python and Scikit-learn.
• Understand and compare performance of various models and metrics.

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🧰 Tools & Libraries

• Python
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn
• TensorFlow / Keras
• OpenCV
• XGBoost / LightGBM
• Google Colab

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🧩 Projects and Tasks

🧮 1. Student Score Prediction

Goal: Predict students’ exam scores based on study hours and related academic factors.
Dataset: Student Performance Dataset – Kaggle

🧠 Process:

• Loaded and cleaned student performance data
• Performed exploratory data analysis (EDA) using Matplotlib and Seaborn
• Trained a Linear Regression model to predict final exam scores
• Evaluated with metrics like R², MAE, and RMSE

📊 Insights:

• Study hours and participation levels had a strong positive correlation with scores
• The Linear Regression model achieved an R² score above 0.85, showing strong predictive power
• Bonus experiments with Polynomial Regression improved results slightly

🧰 Techniques:

Regression | EDA | Feature Engineering | Model Evaluation

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🧭 2. Customer Segmentation

Goal: Group customers into clusters based on annual income and spending behavior.
Dataset: Mall Customer Dataset – Kaggle

🧠 Process:

• Scaled features using StandardScaler
• Applied K-Means Clustering and determined optimal cluster number using the Elbow Method
• Visualized customer groups in 2D space (Income vs. Spending Score)

📊 Insights:

• Identified 5 distinct clusters representing different spending behaviors (e.g., high-income low-spending vs. low-income high-spending)
• Helped visualize how customers differ across spending habits — useful for marketing strategies
• Bonus: Tested DBSCAN clustering for better separation

🧰 Techniques:

Unsupervised Learning | K-Means | DBSCAN | Data Visualization

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💳 3. Loan Approval Prediction

Goal: Predict whether a bank loan application will be approved based on applicant information.
Dataset: Loan Approval Prediction Dataset – Kaggle

🧠 Process:

• Handled missing values and categorical features using Label Encoding and One-Hot Encoding
• Split the dataset into training/testing subsets
• Trained and compared Logistic Regression, Decision Tree, and Random Forest classifiers

📊 Insights:

• Random Forest achieved the highest accuracy (~95%), outperforming Logistic Regression
• Gender, ApplicantIncome, and Credit_History were key factors influencing predictions
• Used SMOTE to handle class imbalance and improve recall

🧰 Techniques:

Binary Classification | Data Encoding | Imbalanced Learning | Evaluation Metrics

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🎬 4. Movie Recommendation System

Goal: Build a movie recommender using collaborative filtering techniques.
Dataset: MovieLens 100K Dataset – Kaggle

🧠 Process:

• Created a user-item rating matrix
• Computed similarity scores using cosine similarity between users
• Recommended top-rated unseen movies based on similar users’ preferences

📊 Insights:

• Successfully recommended personalized movie lists using user-based collaborative filtering
• Experimented with item-based filtering and SVD matrix factorization for improved performance
• Evaluated recommendations using Precision@K

🧰 Techniques:

Recommendation System | Collaborative Filtering | Cosine Similarity | Matrix Factorization

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🚦 5. Traffic Sign Recognition

Goal: Classify German traffic signs using Convolutional Neural Networks (CNN).
Dataset: GTSRB – German Traffic Sign Recognition Benchmark

🧠 Process:

• Preprocessed images (resizing, normalization)
• Built a custom CNN using Keras
• Trained the model on 40+ sign categories
• Evaluated performance using accuracy and confusion matrix

📊 Insights:

• The CNN achieved an accuracy of 98% on the test set
• Using data augmentation improved generalization

🧰 Techniques:

Deep Learning | CNN | Image Preprocessing | Transfer Learning

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📊 Evaluation Metrics Used

Category	Metrics
Regression	R², MAE, RMSE
Classification	Accuracy, Precision, Recall, F1-score
Clustering	Silhouette Score, Inertia
Recommendation	Precision@K
Deep Learning	Accuracy, Loss Curve, Confusion Matrix

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🧪 How to Run Locally

1. Clone the repository:

   git clone https://github.com/Bekamgenene/Elevvo-Internship-Program.git
   cd Elevvo-Internship-Program