arhamansari Goto Github PK

-los-angeles-traffic-collision-data

This dataset reflects traffic collision incidents in the City of Los Angeles dating back to 2010. This data is transcribed from original traffic reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database.

2020-interns

2020 Intern Evaluation Test

analayse-a-b-test-result

Udacity Data Analyst Nanodegree Project: Analyze-AB-Test-Results

analyzing-a-or-b-test.

Case study of a company Audacity

apriori-algo

Understanding Apriori alfgorithm

brain-tumor-detection

About the Brain MRI Images dataset: The dataset contains 2 folders: yes and no which contains 253 Brain MRI Images. The folder yes contains 155 Brain MRI Images that are tumorous and the folder no contains 98 Brain MRI Images that are non-tumorous. You can find it here.

certificate-udacitys-data-analysis

certificates

coding_ninjas_data_sturctures_in_cpp

coding_ninjas_intro_to_cpp

customer_report_website_scrapping

Scrapping product_information for research purpose

data-visualization

data-wrangling

developing-strategy

exploring-the-weather-trends

fiilli_cafe-data-analysis

Internship related work

flags-in-trading

heirachial-clustering

Performing Heirachial Clustering on Mall Dataset

imdb-data-analysis

Making of Dashboard using Ms Excel Pivot Table

instagram_photo_scraping

Scraping Instagram using Python libraries like Selenium,BeautifulSoup,Requests.

internship_loan_data_analysis

This company is the largest online loan marketplace, facilitating personal loans, business loans, and financing of medical procedures. Borrowers can easily access lower interest rate loans through a fastonline interface. Like most other lending companies, lending loans to ‘risky’ applicants is the largest source of financial loss (called credit loss). The credit loss is the amount of money lost by the lender when the borrower refuses to pay or runs away with the money owed. In other words, borrowers who default cause the largest amount of loss to the lenders. In this case, the customers labelled as 'charged-off' are the 'defaulters'. If one is able to identify these risky loan applicants, then such loans can be reduced thereby cutting down the amount of credit loss. Identification of such applicants using EDA is the aim of this case study. In other words, the company wants to understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default. The company can utilize this knowledge for its portfolio and risk assessment.

investigate-tmdb-movies-dataset

Udacity Nanodegree Project

kmeans_clustering

Clean Code to Understand How k-means clustering actually works

loan-prediction

nlp

Using NLP to build ML model which can predict based on comments that reviews are positive or negative

proposal-

About Intern Proposal to startup.

quandl-1day-candle-chart

reliance_quandl

restaurant-website

scraping-coding-bat-questions

Scraping Coding Bat Questions