Mahamad-Jameer-Makandar's repositories
Hand_Written_Digit_Recognition_Using_ML-Deep_Learning_Approach-
I developed a deep learning model using convolutional neural network architecture for recognizing handwritten digits with the MNIST dataset, training it for 10 epochs. With the help of hyperparameter tuning and data augmentation techniques, the model achieved 70% accuracy on the test dataset.
Data-Visualization-on-Honey-Production-dataset-using-seaborn-and-matplotlib-libraries
Explore honey production dynamics (1998-2012) in the U.S. amid declining bee populations using Python's seaborn and matplotlib. Visualize key attributes like colonies, yield, production, price, and stocks to draw insights into the impact on American honey agriculture.
EDA-Data-Preprocessing-on-Google-App-Store-Rating-Dataset
Demonstrate proficiency in Python, Pandas, NumPy, and scikit-learn for data cleaning, EDA, and preprocessing. Key skills include transformation, standardization, outlier handling, and categorical encoding for effective machine learning data preparation.
HBFC-BANK-PERSONAL-LOAN
HBFC Bank's project analyzes 5000 customer records, aiming to convert depositors into personal loan customers. Advanced skills in statistical analysis and tools like Excel uncover key insights, including a 9.6% personal loan uptake. Diverse demographics and strategic findings inform recommendations for future marketing campaigns.
Netflix-Dashboard
Netflix dashboard presents show details: ID, type, title, director, cast, country, date added, release year, rating, duration, genres, and description for an immersive viewing experience.
Pie-in-the-Sky-IPL-Match-Bidding-App-
"IPL Match Bidding App - Pie-in-the-Sky" is a SQL tool decoding IPL match data. It analyzes bidder success, popular stadiums, and toss impact. Tailored for IPL decision-makers, it simplifies complex data for strategic insights in just a few clicks.
TERRO-S-REAL-ESTATE-AGENCY
Terro's Real Estate analyzes Boston house prices with a 506-house dataset. Initial models with LSTAT set baselines; subsequent models refine predictions, achieving an impressive adjusted R-squared of 0.6887. Insights guide precise pricing, showcasing Terro's commitment to industry standards. Addressing challenges enriches the project's narrative.
Travego-Travellers
Implemented MySQL schema for Travego Travellers, recording diverse passenger data and travel prices. Proficient in SQL commands, the project enhances decision-making for travel planning and pricing strategies.