Project	Libraries	Module
Profitable App Profiles for the App Store and Google Play Markets	csv reader	Python for Data Science: Fundamentals
Exploring Hacker News Posts.	csv datetime	Python for Data Science: Intermediate
Exploring eBay Car Sales Data	pandas matplotlib, datetime	Pandas and NumPy Fundamentals
Finding Heavy Traffic Indicators on I-94	pandas matplotlib datetime	Data Visualization Fundamentals
Storytelling Data Visualization on Exchange Rates	pandas matplotlib datetime numpy	Storytelling Data Visualization and Information Design
Analyzing CIA Factbook Data Using SQL	sqlite	SQL Fundamentals
Clean and Analyze Employee Exit Surveys	pandas matplotlib numpy	Data Cleaning and Analysis
Investigating Fandango Movie Ratings	pandas, matplotlib, numpy	Statistics Fundamentals
Predicting car prices using attributes using machine learning	pandas matplotlib numpy KNeighborsRegressor mean_squared_error	Machine Learning Fundamentals

Dataquest Projects

1. Profitable App Profiles for the App Store and Google Play Markets

Data Cleaning

1.   Remove innacurate data
   
2.   Remove duplicates
   
3.   Remove non english apps
   
4.   Isolate free apps
   

Data analysis

1.   Most popular apps by genre on App store
   
2.   Most popular apps by genre on Google play store
   

    Libraries used :| csv, reader

2. Exploring Hacker News Posts.

  Objective is to compare between Ask HN and Show HN posts based on the frequency during different hours.

    Libraries used : csv, datetime

3. Exploring eBay Car Sales Data

  The aim of this project is to clean the data and analyze the included used car listings

1.   Explore date values
2.   Explore registration year
3.   Explore Price by brand
4.   Explore Mileage by brand

    Libraries used : pandas, matplotlib, datetime

4. Finding Heavy Traffic Indicators on I-94

Aim : Using the dataset of I-94 Interstate highway we are going to look at the factors that causes slowness in traffic in this notebook

1.   Split the traffic to day and night data to analyse futher on the influence for traffic
2.   Split the data to different months and analyse if the traffic slowness is different for each month
3.   Analyse traffic slowness for different weather conditions

    Libraries used : pandas, matplotlib, datetime

5. Storytelling Data Visualization on Exchange Rates

Aim : The dataset we'll use describes Euro daily exchange rates between 1999 and 2021. Look at the impact of black swan events .

1.   Mark vertical lines for the event days
2.   Data story visualization based on the price movements

    Libraries used : pandas, matplotlib, datetime, numpy

6. Analyzing CIA Factbook Data Using SQL

1.   Query the database for insights

    Libraries used : sqlite

7. Clean and Analyze Employee Exit Surveys

Aim : Understand why employees are resigning .Is there some kind of a dissatisfaction and is the trend common between experienced and new employees?

• Explored the data and figured out how to prepare it for analysis

• Corrected some of the missing values

• Dropped any data not needed for our analysis

• Renamed our columns

• Verified the quality of our data

• Created a new institute_service column

• Cleaned the Contributing Factors columns

• Created a new column indicating if an employee resigned because they were dissatisfied in some way

• Combined the data

• Cleaned the institute_service column

• Handled the missing values in the dissatisfied column

• Aggregated the data

      Libraries used : pandas,matplotlib,numpy

8. Investigating Fandango Movie Ratings

In October 2015, a data journalist named Walt Hickey analyzed movie ratings data and found strong evidence to suggest that Fandango's rating system was biased and dishonest (Fandango is an online movie ratings aggregator). He published his analysis in this article — a great piece of data journalism that's totally worth reading.

Aim : We are analysing the recent movie ratings data to determine if Fadango's rating system has had any change unlike from 2015.

• Collected the datasets

• Classify the datasets based on the year

• Plot the distribution for year 2015 and 2016

• Calculate the summary metrics and plot the same for the years 2015 and 2016

      Libraries used : pandas,matplotlib,numpy

9. Predicting Car Prices using machine learning (KNN)

Using the attributes available from the dataset (UCI machine learning repository) we try to predict car prices. In this project I create a model using KNN algorithm .

• Univariate model
• Multivariate model
• Hyperparameter tuning