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India is one of the countries with the highest air pollution country. Generally, air pollution is assessed by PM value or air quality index value. For my further analysis, I have selected PM-2.5 value to determine the air quality prediction and the India-Bangalore region. Also, the data was collected through web scraping with the help of Beautiful Soup.
Python package to simplify plotting of common evaluation metrics for regression models. Metrics included are pearson correlation coefficient (r), coefficient of determination (r-squared), mean squared error (mse), root mean squared error(rmse), root mean squared relative error (rmsre), mean absolute error (mae), mean absolute percentage error (mape), etc.
This repository has been created for air pollution forecast in the coming hours in Beijing.
Stock Price Prediction of APPLE Using Python
Rank 6/85 AnalyticsVidhya
Predict solar generation data
Improved the accuracy of Bitcoin stock price predictions on ARIMA model by reducing the seasonality factor. Achieved RMSE value of 68.99 after implementation of SARIMAX model to reduce seasonality.
UV matrix decompostion using movielens dataset
Numerical Methods: "Life Expectancy & Linear Regression" Group Project - 2nd Semester 2021 - Computer Science, UBA
Predicting Delivery Time Using Sorting Time
Tutorials for BSE classes.
Stock Market Prediction Algorithm Implementation for major giants of India
Data Analysis and Prediction of New York Taxi Trip Duration Using Machine Learning Models
Simple Linear Regression
Detecting anomaly in SMS sending behaviour of a person by Particle Swarm Optimization
Designed a predictive model using XGBOOST, Random Forest and evaluated using RMSE and R2 score
Regression predictive analytics to predict food delivery time from restaurant to delivery point
Michigan Covid-19 prediction
Time Series Forecasting - Bus Usage Prediction
đź’Ž 'The Linear Regression Challenge: Diamonds Price Prediction' @ironhack Data Analytics Bootcamp
Build a machine learning/deep learning approach to forecast the total energy demand on an hourly basis for the next 3 years based on past trends.
Forecasting_Airline_Passengers_Seats
Sober truths: Predict the number of fatalities and alcohol-impaired driving crashes
This article uses 2 important models for the predictions and comparisons. These are Long Short-Term Memory and Recurrent Neural Network measures. The result of LSTM and RNN are compared to check the most optimal model for stock forecasting. For this, various metrics and visualization are considered using different independent variables for both the models. We are going to estimate this using different plot criteria, RMSE value, and R2 score of different number of independent variables for both LSTM and RNN.
Giving a song dataset, thorough exploratory analysis, diverse model construction, and innovative feature engineering to developing predictive models for song scoring.
Global Al Hub - Aygaz Machine Learning
This repository is dedicated to my participation in Datatalks Mlzoomcamp
Sweet Lift Taxi collected airport order data. As a Data Scientist, I developed a model to predict taxi orders for the next hour. The goal is to draw more drivers at peak times, targeting an RMSE under 48 on the test set.
Forecasting Wine Sales of Two Different types of Wine. After thorough Data Analysis, different models have been used and tested such as Exponential Smoothing Models, Regression, Naive Forecast, Simple Average, Moving Average. Stationarity of the data is checked. Automated Version of ARIMA/SARIMA Model built. Comparison of Models.
Predicting turbine energy yield (TEY) using ambient variables as features.
Forecasting footsteps in Walmart from previous years available timeseries data and predict on new years data.
Python, K-fold, RMSE, Pearson