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Credit card fraud detection analysis (Python)
Explored a credit card transactions dataset with Python to understand how fraudulent transactions differ from legitimate ones.
Details / full description:
Cleaned and prepared transaction data using pandas, focusing on key numerical features.
Visualized the distribution of fraudulent transaction amounts and compared them with normal transactions.
Calculated summary statistics (mean, median, ranges) to highlight unusual spending patterns.
Interpreted the results to show how this type of analysis can support fraud‑risk monitoring and model development.
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Sentiment-Driven E-commerce Optimization: Amazon Review Analysis & Rating Prediction.
Project Overview ✅
This project serves as a machine learning proof of concept designed to transform raw Amazon product reviews into actionable business insights. By automating the prediction of review ratings and analyzing customer sentiment, the system enables brands to optimize product listings, proactively address customer pain points, and drive higher conversion rates.
Process ✅
I developed an end-to-end pipeline covering data acquisition, complex text processing, and model deployment. Automated Data Scraping, Integrated the Apify API to extract real-time customer feedback directly from Amazon product URLs. I configured the scraper to handle up to 100 reviews per run, capturing critical metadata including rating scores, review descriptions, and verified purchase status.
Data Refinement & Feature Engineering:
Cleaned a dataset of approximately 1,944 reviews by removing noise (punctuation/symbols) and stop words using NLTK. I implemented TF-IDF Vectorization to convert text into numerical features and applied SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, ensuring the model could accurately predict rare negative reviews.
Model Benchmarking ✅
Developed and compared three distinct architectures to identify the most robust predictor:
Naive Bayes: High-speed probabilistic classification.
Support Vector Classifier (SVC): Optimized for high-dimensional text data.
Neural Network (MLPClassifier): To capture complex semantic patterns.
Web App Deployment: Built a dedicated Streamlit dashboard that allows non-technical stakeholders to input raw review text and receive instant rating predictions with confidence scores.
Technical Stack✅
Languages & Tools: Python, Apify Client.
ML & NLP Libraries: Scikit-learn (SVC, Naive Bayes, MLP), NLTK (Tokenization, Stopwords), Imbalanced-learn (SMOTE).
Deployment: Streamlit, Joblib (Model Serialization).
Visualization: Plotly, WordCloud, Matplotlib.
Key Results ✅
Achieved a peak accuracy of 95.27% using the Neural Network model, with the SVC model following closely at 94.46%.
Developed sentiment-based feedback loops within the app: high ratings (4-5 stars) trigger positive marketing recommendations, while low ratings (1-2 stars) alert teams to address product issues like battery life or build quality.
Enabled real-time competitive analysis by providing a user-friendly interface for cross-functional marketing and product development teams to audit customer sentiment at scale.
dashboardPythonmachinelearningMachine Learning EngineeringData AnalysisData VisualizationApifyStreamlit
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End-to-End Machine Learning Pipeline for Telecom Customer Churn
1. The Business Problem
Customer churn is a major challenge for telecommunications companies, driven by competition, service issues, and changing consumer preferences. This project was designed to transition the company from reactive support to proactive retention using data-driven strategies such as customer segmentation, personalized offers, and loyalty programs,.
2. Data Exploration & Insights (EDA)
I performed a comprehensive descriptive analysis on a database of 7,043 customers with 21 distinct variables,. Key findings included:
Contractual Risk: Customers on month-to-month contracts showed significantly higher churn compared to those on one- or two-year commitments,.
Service Preference: While Fiber Optic plans were the most popular, they also represented a critical segment for monitoring due to their higher price points,.
Financial Indicators: Churned customers had a higher average monthly charge of $74.44, compared to $61.27 for retained customers.
Payment Behavior: The "Electronic Check" payment method was most strongly associated with service cancellation,.
3. Engineering & Preprocessing Pipeline
To prepare the data for high-performance modeling, I implemented a rigorous preprocessing workflow:
Data Cleaning: Removed irrelevant identifiers like customerID and addressed potential data quality issues. The dataset was verified to have zero missing or NaN values,.
Feature Engineering: Applied Label Encoding to transform categorical text variables into a numerical format suitable for machine learning algorithms,.
Data Splitting: Adopted a standard 80/20 train-test split to ensure the model could generalize effectively to unseen data,.
4. Model Development & Benchmarking
I developed and benchmarked eight distinct machine learning algorithms to identify the most effective solution for this specific application:
Linear & Probabilistic: Logistic Regression, Naive Bayes.
Tree-Based: Decision Tree, Random Forest.
Boosting Frameworks: AdaBoost, Gradient Boosting, XGBoost, and LightGBM,.
5. Performance Evaluation & Results
Models were evaluated using ROC curves, confusion matrices, and detailed classification reports,.
Winner: Logistic Regression achieved the highest accuracy at 81.83%,.
Secondary Performers: Gradient Boosting (81.05%) and AdaBoost (80.98%) also showed strong predictive power.
6. Technical Conclusion
This data-driven approach proves that proactive churn prediction is essential for business sustainability. By identifying that customers prioritize high-speed fiber optic services but are sensitive to pricing and contract terms, the company can now optimize its pricing and retention strategies to maximize user satisfaction and revenue.
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