Data Science AI Projects

🔟 Data Science Project Ideas for Beginners 👇👇 https://t.me/datasciencefun/1804

How do you stay updated with the latest advancements in machine learning and AI? Share your methods or favorite resources below! 👇

This is a simple example of ML Project 👇👇

What is your favorite machine learning project that you've worked on, and what made it memorable? Share your experience below! 👇

Here are some of the most popular python project ideas: 💡 Simple Calculator Text-Based Adventure Game Number Guessing Game Password Generator Dice Rolling Simulator Mad Libs Generator Currency Converter Leap Year Checker Word Counter Quiz Program Email Slicer Rock-Paper-Scissors Game Web Scraper (Simple) Text Analyzer Interest Calculator Unit Converter Simple Drawing Program File Organizer BMI Calculator Tic-Tac-Toe Game To-Do List Application Inspirational Quote Generator Task Automation Script Simple Weather App Automate data cleaning and analysis (EDA) Sales analysis Sentiment analysis Price prediction Customer Segmentation Time series forecasting Image classification Spam email detection Credit card fraud detection Market basket analysis NLP, etc These are just starting points. Feel free to explore, combine ideas, and personalize your projects based on your interest and skills. 🎯

Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself. 1. Basic python and statistics Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness Automobile :- https://www.kaggle.com/toramky/automobile-dataset 2. Advanced Statistics Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset 3. Supervised Learning a) Regression Problems How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview b) Classification problems Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview Titanic :- https://www.kaggle.com/c/titanic San Francisco crime:- https://www.kaggle.com/c/sf-crime Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification Categorize cusine:- https://www.kaggle.com/c/whats-cooking 4. Some helpful Data science projects for beginners https://www.kaggle.com/c/house-prices-advanced-regression-techniques https://www.kaggle.com/c/digit-recognizer https://www.kaggle.com/c/titanic 5. Intermediate Level Data science Projects Black Friday Data : https://www.kaggle.com/sdolezel/black-friday Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset Million Song Data : https://www.kaggle.com/c/msdchallenge Census Income Data : https://www.kaggle.com/c/census-income/data Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2 Share with credits: https://t.me/sqlproject ENJOY LEARNING 👍👍

Has anyone went through interview for data science related roles recently? Feel free to share your experience 😄

In today’s world, it’s crucial to focus on leading technologies like full-stack development or AI/ML. However, many students are just copying projects instead of learning. To succeed, it’s important to work on real, hands-on projects and truly understand the concepts.

What is your preferred method for handling imbalanced datasets in machine learning? 1. Resampling techniques (oversampling/undersampling) 2. Synthetic data generation (SMOTE, ADASYN) 3. Algorithm-specific techniques (class weights, cost-sensitive learning) 4. Ensemble methods (bagging, boosting) 5. Other (share your approach in the comments below!) 👇👇

9 Distance Metrics used in Data Science & Machine Learning. In data science, distance measures are crucial for various tasks such as clustering, classification, and regression. Below are nine commonly used distance methods: 1. Euclidean Distance: This measures the straight-line distance between two points in space, similar to measuring with a ruler. 2. Manhattan Distance (L1 Norm): This distance is calculated by summing the absolute differences between the coordinates of the points, similar to navigating a grid-like city layout. 3. Minkowski Distance: A general form of distance measurement that includes both Euclidean and Manhattan distances as special cases, depending on a parameter. 4. Chebyshev Distance: This measures the maximum absolute difference between coordinates of the points, akin to the greatest difference along any dimension. 5. Cosine Similarity: This assesses how similar two vectors are based on the angle between them, used to measure similarity rather than distance. For distance, it's often inverted. 6. Hamming Distance: This counts the number of positions at which corresponding symbols differ, commonly used for comparing strings or binary data. 7. Jaccard Distance: This measures the dissimilarity between two sets by comparing the size of their intersection relative to their union. 8. Mahalanobis Distance: This measures the distance between a point and a distribution, accounting for correlations among variables, making it useful for multivariate data. 9. Bray-Curtis Distance: This measures dissimilarity between two samples based on the differences in counts or proportions, often used in ecological and environmental studies. These distance measures are essential tools in data science for tasks such as clustering, classification, and pattern recognition.