Ayush S Pangaonkar (bluntjudg)

bluntjudg

Geek Repo

Location:Mumbai

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Ayush S Pangaonkar's repositories

Admissions-Predictions

This projecct describes the process whether the students are eligible for admission criteria based on the random data created with the help of python

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bluntjudg

Config files for my GitHub profile.

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Book-Recommendation-System-

This project uses machine learning to create a personalized bookrecommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and book attributes to suggest tailored book recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.

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Diabeties-prediction-all-models-

Diabetes prediction using machine learning involves developing models to forecast diabetes onset based on patient data like age, BMI, blood pressure, and glucose levels. Techniques include logistic regression, decision trees, and neural networks, enhancing early diagnosis and personalized treatment plans.

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face-detection-

This is a project of python where the faces of humans are detected

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Gender_Classification

A machine learning project for gender classification using various algorithms. It leverages features such as age and height to predict gender, aiming for high accuracy and efficiency. Includes data preparation, model training, and evaluation.

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Graphical-User-Interference-Python

this repo contains example of basics of gui projects to get hands on with python

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House-Price-Predictions-

This project is divided into Train dataset and Test dataset and prediction is based on both the dataset to cross check the sales price and sales conditions

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Iris---classification-

Iris project classification - Machine Learning

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Movie-Recommendation-system---python-and-juypter-

This project uses machine learning to create a personalized movie recommendation system. By combining collaborative filtering and content-based filtering, it analyzes user preferences and movie attributes to suggest tailored movie recommendations. The system offers real-time updates and accurate predictions to enhance the user experience.

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Play-with-python-javascript-html

This repo contains interactive GUI's and python games code

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python-examples

basic to advanced questions based on python

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Spam-Classifier-

Integrated with Python's Scikit-learn, ensured robust feature extraction and model evaluation, enhancing email security.

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Speed-distance-model-

Speed distance model - to check the model accuracy - a supervised learning model example

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Text-Sentiment-Analysis-

Dataset includes the sentences used in our daily conversation that are classified into the emotions

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Titanic---Prediction-classification-

In our Titanic dataset project, we predicted passenger survival using machine learning. We cleaned the data, handled missing values, and engineered features like FamilySize. After exploratory analysis, we trained models, with Random Forests achieving 80% accuracy, highlighting key survival factors.

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