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a-b-testing-results

Practiced working with some of the difficulties for understanding and interpreting the results of the A/B test run by an e-commerce website.

adult-census-income

Predict whether income exceeds $50K/yr based on census data

analyze-movies

This project is all about clean and analyze data set for tmdb movies and extract useful information from it then communicate results

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

churn-analytics-dashboard

A fictional telco company that provided home phone and Internet services to 7043 customers in California in Q3.

complete-guide-to-data-visualization

The main libraries for data visualization with Python and all the types of charts that can be done with them. We will also see which library is recommended to use on each occasion and the unique capabilities of each library.

contosoretaildwh-w--python

It's will be a great Journey to Walkthrough this Project and discover a lot of information about Retail Business, Market, Target Customer, Geo, Brands, Sales and Forecasting

cookiecutter-modern-datascience

Start a data science project with modern tools

crud-app

Create, update and delete from SQL Server using Flask

data-structure-and-algorithms

database-connection-setup-from-python-

Connect to teradata or any DB using Pyodbc and teradatasql and Fetch your data to DataFrame using Pandas

diamonds

For the case study, will concentrate only the variables in the top five bullet points: price and the four 'C's of diamond grade. Our focus will be on answering the question about the degree of importance that each of these quality measures has on the pricing of a diamond, and make prediction using linear regression algorithm

finding_donors

In this project, I will employ several supervised algorithms of my choice to accurately model individuals' income using data collected from the 1994 U.S. Census. I will then choose the best candidate algorithm from preliminary results and further optimize this algorithm to best model the data. my goal with this implementation is to construct a model that accurately predicts whether an individual makes more than $50,000. This sort of task can arise in a non-profit setting, where organizations survive on donations. Understanding an individual's income can help a non-profit better understand how large of a donation to request, or whether or not they should reach out to begin with. While it can be difficult to determine an individual's general income bracket directly from public sources, we can (as we will see) infer this value from other publically available features.

home-loan-eda-and-prediction

Dream Housing Finance company deals in all home loans. They have presence across all urban, semi urban and rural areas. Customer first apply for home loan after that company validates the customer eligibility for loan. Company wants to automate the loan eligibility process (real time) based on customer detail provided while filling online application form. These details are Gender, Marital Status, Education, Number of Dependents, Income, Loan Amount, Credit History and others. To automate this process, they have given a problem to identify the customers segments, those are eligible for loan amount so that they can specifically target these customers.

introduction-to-data-visualizations

Let’s see the main libraries for data visualization with Python and all the types of charts that can be done with them. We will also see which library is recommended to use on each occasion and the unique capabilities of each library.

iti-intake41-information

Web scraping with Scrapy framework

job-change-of-data-scientists

EDA and Predict who will move to a new job

lightup-website

This project is a front end website for Lightup CO.

marketsegmentation

mini_projects_w-python

prosper-loans-analysis

Prosper is a peer-to-peer lending platform that aims to connect people who need money with those people who have the money to invest. In this data analysis project, I have explored the Prosper dataset, prepare it for analysis and used 'Pyplot and Seaborn' to create my visualization and communicate results.

retail-datawarehouse-dashboard

1. Geography Analysis and Visualization. 2. Product Analysis and Visualization. 3. Channels and Promotions Analysis and Visualization. 4. Customers (Persons) Analysis and Visualization.

sales-dashboard-power-bi

Historical sales data for 45 stores located in different regions - each store contains a number of departments. The company also runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks

sla

survival-analysis-