My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.
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Updated
Aug 14, 2023 - Jupyter Notebook
My notes in Jupyter Notebooks for statistics, probability, and plotting applied with major python libraries as an introduction to machine learning.
Random Good Data Science Stuff
A trading algorithm that identifies stocks with the largest potential for growth while heavily considering its volatility using quantopian
Time Series Analysis
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…
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.
Analyze online shoppers' purchase intentions using Logistic Regression, K-means clustering & A/B Testing
A regression based modeling project to forecast the sales of Walmart
Data Science: Storytelling and Deployment - analyzing LEGO Database with Streamlit.
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
Udacity Data Analyst Nanodegree - Project III
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.
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.
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).
MLR Statistical Analysis and Prediction of Ames Real Estate Prices with Streamlit
House price prediction using Linear Regression models (scikit learn and statsmodel)
A data analytics project that utilizes PANDAS, Numpy, Matplotlib and statsmodel to analyze the results of hypothesis testing and regression modeling in determining whether a website update should be launched.
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?
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