IPL Inning Score Prediction

Objective:

It focuses on calculating the results of IPL matches using data mining techniques on both balanced and imbalanced datasets. In T20 Cricket matches, the first innings score is currently estimated based on the existing run rate, which is measured as the number of runs scored per a number of overs bowled. It includes the following factors:

1. Number of wickets left
2. Number of balls left
3. On how much scores are the current batsman batting?
4. How much has the team scored in the last 5 years?
5. How much the team have lost wickets in the last 5 overs?
6. The nature of the pitch
7. How strong is the batting and bowling team?

STEPS INVOLVED

1. Firstly, the data is trained. We will take 15-20% of the data collection to train the model.
2. We will take 15-20% of the data from the data collection to train the model.
3. For the prediction, we will be using a Linear regression algorithm.
4. The project is split into three Jupyter Notebooks: one to collect the IPL data, inspect it, and clean it; a second to refine the features further and fit the data to a Linear Regression model to train and evaluate our output.

STEPS INVOLVED IN DATA PREPROCESSING:

1. Feature Selection: We have a lot of unnecessary attributes in our data that we won’t use in our project. As a result, we only use the attributes that we need.
2. Normalization: The initial step is to normalize the data which we have collected from the internet. Rescaling real-valued numeric attributes into the range between 0 and 1 is referred to as normalization. The data is then normalized after it has been filtered.
3. Machine Learning : The method of iteratively refining your prediction equation through looping over the dataset several times by updating the values of weight and bias in the direction suggested by the slope of the gradient (Cost Function) is known as training a model. We consider training to be complete, when we exceed an appropriate error, or when required training iterations (epochs or cycles) fail to reduce our cost.

IMPLEMENTATION OF ALGORITHM:

Linear Regression is the algorithm used in our project.

1. Linear Regression: Regression is the method that measures the average relationship between two or more continuous variables in terms of the response variable and feature variables. Also, in other words, regression analysis is to know the nature of the relationship between two or more variables to use for predicting the most likely value of dependent variables for a given value of independent variables.

RESULT:

The IPL score prediction system works properly. All of the attribute values had been preprocessed correctly. The model was applied and trained using training data after all of the preprocessing was done. The Linear Regression model accuracy was found to be 82%.