Stats: AB Testing with Python
Evaluating landing page performance versus previous version using statistical methods. A rigorous test essential for serious business.
Evaluating landing page performance versus previous version using statistical methods. A rigorous test essential for serious business.
Implement AlexNet, a deep learning architecture, to classify sport images into 5 sports categories.
Sentiment analysis of Amazon reviews using VADER and RoBERTa models, with evaluation via regression metrics and error analysis.
Predicting asteroid danger using stacked ensemble models based on size, speed, and distance data from space agencies.
Using a CNN algorithm to analyze lung scans and detect signs of Covid-19 quickly and accurately.
An interactive Tableau map showing Covid-19 cases across different regions.
Conduct a customer cohort analysis to gain insights into evolving customer behavior over time.
A computer vision-based system that tracks bicep curl repetitions by analyzing joint angles in real time.![]()
A real-time face recognition system that stores a database of faces and identifies them in video streams. Widely used for employee attendance systems.
A simple convolutional neural network trained to classify fashion items.
A machine learning project exploring SHAP for feature interpretability in gender classification. Visualizes feature contributions for transparent model predictions.
Using LSTM to detect offensive language and hate speech in tweets. The model learns patterns of harmful content through diverse datasets.
Analysis and visualization of the Kaggle 2022 Data Science Survey, revealing patterns and trends.
A movie recommendation system combining content and collaborative filtering to suggest movies tailored to your preferences and broaden your cinematic horizons.
A weekend project using computer vision to detect if parking spots are empty.
An interactive sales dashboard presenting key metrics and insights visually for stakeholders.
A model to identify potential suicide ideation in tweets using sentiment analysis and a pre-trained DistilBERT model. Includes interpretation with SHAP.
Using k-means clustering to group customers by behavior for targeted marketing.
Using Meta’s Prophet algorithm for time series forecasting—a data crystal ball analyzing past patterns to predict future trends.