Data Science Projects in Bengaluru
Data Science Projects in Bengaluru
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3
Asif Farhan Khan
pro
Human Value Index: Visualizing Media Bias in Violent Death
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12
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4
Varun Walekar
ProData AI — Automated Data Science Platform Built ProData AI, an automated data science platform developed entirely with Streamlit. It is designed to help users transform raw datasets into actionable business insights in seconds. 🚀 What it does One-Click Mode Upload any CSV or Excel file and the full pipeline runs automatically in under 30 seconds: Data cleaning & preprocessing AutoML (6 models trained simultaneously) 30-day forecasting using Prophet Business driver analysis with Explainable AI (XAI) AI-generated insights using Anthropic Claude PDF report generation Manual Mode Provides full control over each stage of the data science workflow for advanced users. 🛠 Tech Stack Streamlit — complete UI and app framework scikit-learn — AutoML pipeline Prophet — time-series forecasting Anthropic Claude API — AI insights & chat Plotly — interactive visualizations fpdf2 — PDF report generation Ideal for: business analysts startups small businesses automated reporting workflows freelance analytics projects Open to freelance collaborations and custom dashboard / AI reporting solutions.
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4
366
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chandra sekhar
Internal Business Process & Efficiency Scorecard Matrices
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28
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JUNAID ALTAF
LIOTERING DETECTION
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25
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Abhishek Kumar
Abhishek Kumar’s Post
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7
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PADMANAVA PARUI
B2B (Bike Sharing) Demand Forecasting & Seasonality Engine
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85
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Kratik Mehta
EV Market Segmentation in India
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7
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Sai Kumar Reddy N
saikumar0605/Dask-ml
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7
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Srijan Devnath
Electric Car Adoption Analysis
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14
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Narayanan M
301 Moved Permanently
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3
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Shawn Dsouza
WhatFishDo: A Behavior Annotation Tool for Videos
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4
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Amish Bemelkhedkar
Fertilizer recommendation from crop and live weather
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3
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Rishank Gautam
Ris1103/Maths-Problem-Classification
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5
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Mann Acharya
AI Talent Matchmaking System
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14
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Sowmya Lanka
What I built Developed a machine learning solution to predict Buy Box and sales prices using historical analytical data and Linear Regression. How it works Analyzed the data for linear relationships, autocorrelation, multicollinearity, homoscedasticity, and normally distributed errors. Applied Principal Component Analysis (PCA) for dimensionality reduction and to improve model performance. Identified key business factors influencing price prediction. Evaluated the model using R², Adjusted R², RMSE, MAE, and MSE metrics. Technologies Python · Pandas · NumPy · Scikit-learn · Linear Regression · PCA Outcome Built a price prediction model that identified important business drivers and evaluated prediction performance using multiple regression metrics.
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116
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Zakki Syed
Dune Dashboard
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9
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