Machine Learning with Python

This repository is a workspace for learning Artificial Intelligence, Machine Learning, and Deep Learning as a part of my course. The best way to get started is to navigate through the repository or click here for directing to the main website.

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Contents

1. Data Analysis of Mobile apps data
2. 28 May 2019
3. Problem Set using OOPs concepts
4. Pre-processing of the data
5. Day 12 (6.June.19)
6. Machine learning Fundamentals
7. Linear Regression
8. seaborn
9. Day16(11June19)
10. Project: House Sale Price Prediction using Linear Regression

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Data Analysis of Mobile apps data

• Calculate the average rating for free apps
• Calculate the average rating for non-free apps
• Calculate the average rating of Gaming and Non-Gaming apps
• Calculate the average rating of free Gaming apps
• Compute the average rating of the apps whose genre is either "Social Networking" or "Games."
• Compute the average rating of the non-free apps whose genre is either "Social Networking" or "Games."
• Compute the average rating of the apps that have a price greater than $9.
• Categorise all apps by labelling each app as "free"(=0), "affordable" (<20), "expensive" (<50) or "very expensive" (>50). Add a label column to the data.
• Compute the total number of unique apps from the dataset
• Print the Top 10 apps along with their rating based on the number of downloads(rating_count_tot)
• Categorise the dataset based on content rating into the following
  • Number of apps with content rating 4+
  • Number of apps with content rating 9+
  • Number of apps with content rating 12+
  • Number of apps with content rating 17+

Github: Day_01.ipynb

28 May 2019

1. Print no. of matches won by each team based on year wise (each year as one column)
2. Print ManOfMach count of each player in Hyderabad stadium
3. Print number of matches won and loss(as two columns). Consider as win when a team wins Toss and Match . print for each and every team

Github: Day_4(28.May.19)_Matches_Display.ipynb

Problem Set using OOPs concepts

1. Draw a pie plot based on number of wins for each team
2. Draw a bar graph for number of matches won (i,e.consider win if the team wins both the toss and match) for the team “Mumbai Indians” for all years

Github: Day 6 (30 May 19).ipynb

Pre-processing of the data

• Get the dataset
• Importing Libraries
• Importing the dataset
• Missing Values
• Categorical Data
• Splitting Data
• Feature Scaling

Github: Data Munging-Day11(5June19).ipynb

Day 12 (6.June.19)

• Missing Values
• Categorical Data
• Splitting Data
• Feature Scaling

Github: Day12(6June19).ipynb

Machine learning Fundamentals

• Documents regarding Fundamentals of Machine learning.

Linear Regression

Github: Day14(8June19).ipynb

seaborn

Github: Day15(10June19).ipynb

Day16(11June19)

Github: Day16(11June19)

House Sale Price Prediction using Linear Regression

Github: House Sale Price Prediction using Linear Regression

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