Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Model Predictions using simple linear regressi

Data Science: Storytelling and Deployment - analyzing LEGO Database with Streamlit.

In this project I predict the 2016 MLS season using historical data and Poisson regression. The project includes cleaning, preprocessing and analyzing the dataset, building and evaluating predictive models for match outcomes, forecasting team performance and simulating the league table. It uses Pandas, Numpy, MatPlotLib and StatsModel libraries.

MLR Statistical Analysis and Prediction of Ames Real Estate Prices with Streamlit

Human Data Science Course Project A.Y. 2022/2023 @ Alma Mater Studiorum University of Bologna - UNIBO

Assignment-04-Simple-Linear-Regression-1 Q1) Delivery_time -> Predict delivery time using sorting time. Build a simple linear regression model by performing EDA and do necessary transformations and select the best model using R or Python. EDA and Data Visualization, Feature Engineering, Correlation Analysis, Model Building, Model Testing and Mod…

My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.

Model to identify the potential lead by assigning a score for their rate of conversion. Therefore, reaching out to potential is no more a brainstorming task.

Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing

Prediction of nitrogen dioxide concentration in air using linear regression.

I perform a retrospective analysis on the linear regression analysis that I previously performed on the NYC Bike Counts dataset. Specifically, I analyze my linear regression analysis to identify anything that I could have done differently.

I used the New York Bike Counts dataset to formulate a hypothesis about the number of bikes crossing the Brooklyn Bridge. This dataset contains the number of bikes that crossed each bridge during each day. I first used this dataset to formulate a hypothesis and then used linear regression to test if my hypothesis was correct.

The objective is to build a ML-based solution (linear regression model) to develop a dynamic pricing strategy for used and refurbished smartphones, identifying factors that significantly influence it.

This dataset provided by Udacity in their data analyst course proivdes a walkthrough of an AB test for customer conversion.

A/B tests are very commonly performed by data analysts and data scientists. It is important that you get some practice working with the difficulties of these.

I am interested in predicting whether an individual will default on his or her credit card payment, on the basis of annual income and monthly credit card balance. First I will use Logistic regression with 1 feature only (balance) and then multiple logistic regression with 2 features (balance and income).

A regression based modeling project to forecast the sales of Walmart

This repository describes the implementation of Machine Learning techinques using the Statsmodels pacakge

Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?