Taimoor Khan's Projects
This project conducts A/B Testing for an e-commerce company to evaluate the effectiveness of a new landing page compared to the old one. The analysis employs both Frequentist and Bayesian approaches, providing insights into user conversion rates. The results guide recommendations on whether to adopt the new page or stick with the existing one.
This is a basic analysis of the wild fire activities that occur in Australia. The analysis is done by using Matplotlib, Seaborn, and Folium while the dashboard is created using Dash..
Highlights the practical project completed during the IBM Data Science Applied Data Science Capstone module. Explore real-world applications showcasing skills in data analysis, machine learning, and data visualization
African Development Website
Discover 'Precision in Projections' by Muhammad Taimoor Khan, unraveling Australian rainfall forecasts. Leveraging advanced models, the project attains high accuracies, with Logistic Regression leading at 83.7%. A vital resource for data-driven decisions in Australia's ever-changing climate.
Basic Chatbot Using Python with just basic responses
This is my Credit Card Fraud Detection project, I use machine learning algorithms to analyze transaction patterns, spotting potential fraud by considering factors like amount, location, and user behavior. This proactive approach helps prevent credit card fraud, safeguarding both cardholders and financial institutions.
Practical work and final lab exam of IBM Data Analysis with Python Course.
My First Data Science Project where I used Python to analyze the historical stock/revenue data of Tesla and Gamestop.
my practical work for the course "Data Visualization Using Python" offered by IBM.
Practical work of Database and SQL for Data Science Integrated with Python.
Emotion Detector, by Muhammad Taimoor Khan, is a real-time facial emotion recognition system using a pre-trained model. The project, featuring videotester.py for live detection and emotion_detector.ipynb for insights, offers a high-performance pre-trained model (best_model.h5) for seamless integration into applications requiring emotion recognition
This is a Command Line game of "Hangman" developed using Python and Natural Language Tool Kit (NLTK)
All the submissions I did in my IBM Data Science course.
Estimate laptop prices with confidence using this streamlined predictor. Leverage exploratory data analysis, machine learning models, and a user-friendly Streamlit app to make informed purchasing decisions. Explore the project's notebook for in-depth insights and predictions.
Repository of all the tasks I performed during my time as a Data Science Intern at Let's Grow More.
This project successfully utilizes K-Means Clustering to categorize mall customers into distinct segments. The visualization of clusters and centroids provides a clear understanding of customer distribution, enabling businesses to make informed decisions for targeted marketing and service strategies.
A Tool For Analyzing Text in Django.
"Titanic Survival Prediction" uses machine learning to forecast passenger survival on the iconic ship. Achieving 80% and 78% accuracy with Logistic Regression, and an impressive 98% and 82% with Random Forest Classifier on training and testing data, explore the code for insights into historical data and predictive modeling.
Twitter Sentiment Analysis using ML by Muhammad Taimoor Khan. Trained Logistic Regression model achieves 81% accuracy on training data and 77% on testing data. Code includes data preprocessing, stemming, vectorization, and model evaluation. Explore sentiments in tweets effortlessly.
This is a URL Shortener application developed using Python and the GUI is developed using Tkinter Library of Python
This is a Basic User Taught ChatBot (teachable by the user) developed using Python and JSON only.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent