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The best prediction models that win big this IPL 

The Indian Premier League (IPL) is the most popular and exciting cricket league in the world. There will be ten teams competing against each other this IPL season 2022 for the trophy. Since the start of IPL in 2008, it has grabbed the attention and interest of people from all over the globe. The high level of uncertainty of the matches, and last-minute wins and losses have only increased the viewer count for this cricketing event over the years. And who doesn’t like knowing which team will win that particular match, or even the entire season in advance? Between the matches, you might have also seen a scoreline at the bottom of your television screens showing the winning probability of the two teams. You’d have also seen the probability changing every over for the teams. That is based on the number of runs scored and wickets taken in that particular over. All these predictions are made with the help of data analytics, deep learning, and machine learning. 

Humans cannot analyze extremely huge sets of data, and that is where data analytics and machine learning come into the picture. Predicting the result of an IPL match is a massive work. It includes consolidating the data, analyzing it, and then predicting the result through the number of runs scored, wickets taken, wins and losses of any particular team from the past seasons, and so much more. Imagine the amount of data one would have to analyze for that! Data Analytics makes the work a lot easier.

Data Analytics and Data Science are not rocket science. It only gets easier and more interesting as one starts pursuing it. Doing a data analytics course would help any individual predict the wins of the IPL matches forever! Most of the data science and data analytics courses can also be done online, in the comfort and convenience of one’s own house. Learn Data Analytics online with Imarticus.

STEP-BY-STEP IMPLEMENTATION OF PREDICTION MODEL:

Step 1: Data extraction!

Extract a data sheet that contains all the details of every IPL player from as many seasons as possible.

Step 2: Data cleaning and formatting

Keep the required data sets only.

Step 3: Encoding the categorical data to numerical values.

Encode the raw data into numerical values that make sense to the computer.

Step 4: Feature Engineering and Selection

Divide the data into train sets and test sets before using a machine learning algorithm. Also, scale the data before processing it to make the model less complicated.

Step 5: Building, Training & Testing the Model

Building the correct prediction model using a computer language is crucial. The model can use functions like Sequential and mean squared error, and algorithms like Adam Optimizer, etc. The prediction models can be of a wide range, and multiple kinds of functions and algorithms can be used.

Step 6: Prediction

Create a data frame that shows the actual values and the predicted values. If done correctly, the model will predict the results of the IPL matches at maximum accuracy. It will give almost similar scores. To find out the difference between the actual and predicted scores more accurately, performance metrics will show the error rate using mean_absolute_error and mean_squared_error. 

 Imagine how much time it would have taken us to do all this! But as it can be seen, the above steps in a procedural manner can simplify problem-solving and are generally preferred in the industry. Predicting an IPL match can be as easy for you too! Learn Data Science and Data Analytics courses online with Imarticus

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