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Cricbuzz is an Indian cricket news website owned by Times Internet, It is a one-stop for all cricket enthusiasts for all cricket news, live scores. This includes scorecards, videos, text commentary, team rankings, and player seats. Where Hotstar is used for watching the live match, the cricbuzz is used by people on the move, who want to know the live score. CricBuzz is one of the most popular mobile apps for cricket news and scores in India.



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Cricbuzz has acquired match outcomes data over the last 4 years, using this they want to create a service that will predict who will win a match. They need a model which will take all the factors as input, and predict who will win the match. For doing so, we will perform the following steps:

1. EDA (Exploratory Data Analysis)

We will analyze data by generating some graphs using matplotlib and seaborn.

2. Data Cleaning

Data generally have numerous columns, and some might even have noisy data, so before using it to train a model, we will first clean it

3. Model Training

This is the final step in creating a machine learning model and training it on our cleaned data.



What you'll learn in this live session?

· How to perform Exploratory Data Analysis

· How to use pandas, and scikit-learn for data cleaning

· What is machine learning

· What is a classification model



Who can attend this session?

· Anyone curious to know how a data scientist tackle a problem

· Students with machine learning, deep learning, artificial intelligence, and data science background

· Professionals in computer science, data science, and machine learning.

Core Topics to be introduced in this live session:

What is Exploratory Data Analysis

A data scientist generally receives data in tabular form, and nobody cannot analyze it in that format. Exploratory data analysis is a method or process in data mining through which data is analyzed by plotting data in graphs. This way any person can understand what does the data shows. The graphs generated can be used as evidence to explain to executives. Some of the graphs that data scientists extensively use are:

1. Histograms

2. Box Plots

3. Scatter Plot

4. Line Plot

5. Bar Plot


What you'll learn in this live session?

· How to perform Exploratory Data Analysis

· How to use pandas, and scikit-learn for data cleaning

· What is machine learning

· What is a classification model



Who can attend this session?

· Anyone curious to know how a data scientist tackle a problem

· Students with machine learning, deep learning, artificial intelligence, and data science background

· Professionals in computer science, data science, and machine learning.

Core Topics to be introduced in this live session:


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