Airbnb First Travel Destination
Python for Data Science Introduction
Plotting for exploratory data analysis (EDA)
exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
- Introduction to IRIS dataset and 2D scatter plot
- 3D scatter plot
- Pair plots
- Limitations of pair plots
- Histogram and Introduction to PDF(Probability Density Function)
- Univariate Analysis using PDF
- CDF(Cumulative Distribution Function)
- Mean, Variance and Standard Deviation
- Percentiles and Quantiles
- Box-plot with Whiskers
- Violin Plots
- Summarizing Plots, Univariate, Bivariate and Multivariate analysis
- Multivariate Probability Density, Contour Plot
- Exercise: Perform EDA on Haberman dataset
It will give you the tools to help you with the other areas of mathematics required to understand and build better intuitions for machine learning algorithms.
- Why learn it ?
- Introduction to Vectors(2-D, 3-D, n-D)
- Dot Product and Angle between 2 Vectors
- Projection and Unit Vector
- Equation of a line (2-D), Plane(3-D) and Hyperplane (n-D)
- Equation of a Circle (2-D), Sphere (3-D) and Hypersphere (n-D)
- Equation of an Ellipse (2-D), Ellipsoid (3-D) and Hyperellipsoid (n-D)
Probability and Statistics
Dimensionality reduction and Visualization:
In machine learning and statistics, dimensionality reduction or dimension reduction is the process of reducing the number of random variables under consideration, via obtaining a set of principal variables. It can be divided into feature selection and feature extraction.
PCA(principal component analysis)
(t-SNE)T-distributed Stochastic Neighbourhood Embedding
Real world problem: Predict rating given product reviews on Amazon
Classification And Regression Models: K-Nearest Neighbors
Performance measurement of models
Solving optimization problems : Stochastic Gradient Descent
Support Vector Machines (SVM)
Python for Data Science: Data Structures
Python for Data Science: Functions
Python for Data Science: Miscellaneous
Python for Data Science: OOPS(object oriented program)
Python for Data Science: Pandas
Python for Data Science: Matplotlib
Python for Data Science: Numpy and Scipy
Python for Data Science: Seaborn
Python for Data Science: Scikit Learn
Airbnb is an online marketplace and hospitality service, enabling people to lease or rent short-term lodging including vacation rentals , apartment rentals, homestays , hostels beds, or hotel rooms.
New users on Airbnb can book a place to stay in 34,000+ cities across 190+ countries. By accurately predicting where a new user will book their first travel experience, Airbnb can share more personalized content with their community, decrease the average time to first booking, and better forecast demand. We need to predict the first travel destination of a new user based on his personalized content .
- Data Type:
- CSV files
- age_gender_bkts.csv – summary statistics of users’ age group, gender, country of
- countries.csv – summary statistics of destination countries in this dataset and their locations
- sessions.csv – (user_id , action , action_type , action_detail , device_type , secs_elapsed )
- test_users.csv – ( id , date_account_created , timestamp_first_active ,
date_account_created, date_first_booking , gender , age , signup_method , signup_flow,
language , affiliate_channel , first_affiliate_tracked , signup_app , first_device_type ,
first_browser , country_destination )
- train_users.csv – ( Similar to test data )
- Data Size: 664.2MB
- Validity of this course is 240days( i.e Starts from the date of your registration to this course)
- Expert Guidance, we will try to answer your queries in atmost 24hours
- 10+ machine learning algorithms will be taught in this course.
- No prerequisites– we will teach every thing from basics ( we just expect you to know basic programming)
- Python for Data science is part of the course curriculum.
We are building our course content and teaching methodology to cater to the needs to students at various levels of expertise and varying background skills. This course can be taken by anyone with a working knowledge of a modern programming language like C/C++/Java/Python. We expect the average student to spend at least 5 hours a week over a 6 month period amounting to a 145+ hours of effort. More the effort, better the results. Here is a list of customers who would benefit from our course:
- Undergrad (BS/BTech/BE) students in engineering and science.
- Grad(MS/MTech/ME/MCA) students in engineering and science.
- Working professionals: Software engineers, Business analysts, Product managers, Program managers, Managers, Startup teams building ML products/services.
- Lectures 261
- Quizzes 0
- Duration 70+ hours
- Skill level All levels
- Language English
- Students 1
- Assessments Yes