theadeelahmed/imdb-movies-review-sentiment

Sentiment analysis on IMDB movie reviews using deep learning (ANN) — built during my AI learning journey with TensorFlow/Keras.

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theadeelahmed/Pizza-Sales-Analysis-SQL-Project

A SQL portfolio project analyzing pizza sales data using MySQL. Covered real-world queries like revenue trends, top-selling pizzas, and order behavior using CSV-based dataset.

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theadeelahmed/Complete-SQL-Learning-MySQL

Complete SQL learning with MySQL based on Mam Sharda’s course from my college — includes all topics, practice questions, and notes. Part of my Data Science & Ai.

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theadeelahmed/Space_X_Lauch_analysis_and_Prediction_Platform

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theadeelahmed/emirates-airways-review-analysis

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theadeelahmed/Airline-Customer-Satisfaction-Analysis

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theadeelahmed/twitter_Sentiment_Analysis

"Twitter/X Sentiment Analysis using NLP! Analyze public opinions by processing tweets and classifying sentiments (Positive, Neutral, Negative) with Natural Language Processing. Gain valuable insights into trends and emotions! "

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theadeelahmed/Customer_Funnel_Analysis

Customer Funnel Analysis – Tracking customer behavior at each stage of the sales funnel using Pandas & Matplotlib. Analyze drop-offs, identify trends, and optimize conversions.

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theadeelahmed/Sentiment_analysis

Customer Review Segmentation & Sentiment Analysis using NLP , This project processes and segments customer reviews using NLTK and text preprocessing techniques to identify sentiments and extract key insights. Built with Python and Pandas, it provides a structured approach to analyzing customer feedback.

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theadeelahmed/movie-suggestion-system

Movie Recommendation System A content-based recommendation system that suggests similar movies using cosine similarity and text vectorization (TF-IDF). Built with Python, Pandas, Scikit-Learn, and NLTK.

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theadeelahmed/mnist-dl-model

MNIST Image Classification using Deep Learning (TensorFlow & Keras). A simple model trained on grayscale handwritten digits for accurate predictions.

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theadeelahmed/Mall-customers-Segmention

This project segments mall customers based on income, age, and shopping score. Using clustering techniques, it identifies key customer groups for targeted marketing campaigns. Tools used: Pandas, Matplotlib, Seaborn, and Scikit-learn

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theadeelahmed/Market_Segmentation_Clustering

# Market Segmentation using Clustering This project uses clustering techniques to segment customers for personalized services like savings plans and loans. It provides insights for businesses to target customers more effectively. Technologies: Python, Pandas, Scikit-learn, Matplotlib, Seaborn

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