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Dogecoin Price Prediction with Machine Learning

IPL Score Prediction using Deep Learning

Last Updated : 27 May, 2025

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In today’s world of cricket every run and decision can turn the game around. Using Deep Learning to predict IPL scores during live matches is becoming a game changer. This article shows how advanced algorithms help us to forecast scores with impressive accuracy, giving fans and analysts valuable insights in real time.

Why we using Deep Learning for Score Prediction?

We humans can't easily identify patterns from huge data, deep learning algorithms can do this efficiently. These algorithms learn from how players and teams have performed against each other in the past. Unlike simpler machine learning methods, deep learning can analyze many different factors at once making its score forecasts more reliable and closer to reality.

Now lets implement it step by step:

1. Installing Libraries

We are importing all necessary Python libraries such as NumPy, Pandas, Scikit-learn, Matplotlib, Keras and Seaborn required for data handling, visualization, preprocessing and building deep learning models.

Python

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import preprocessing
import keras 
import tensorflow as tf

2. Loading the Dataset

The dataset can be downloaded from here. It contains data from 2008 to 2017 and contains features like venue, date, batting and bowling team, names of batsman and bowler, wickets and more. We will load the IPL cricket data from CSV files into pandas DataFrames to explore and prepare for modeling.

Python

ipl = pd.read_csv('ipl_dataset.csv')
ipl.head()

Output:

dataset

3. Exploratory Data Analysis

We will do Exploratory Data Analysis (EDA) to analyze how many unique matches have been played at each venue by counting distinct match IDs for every venue. Then, we’ll visualize this data using a horizontal bar chart to see which venues host the most matches.

Python

data = ipl.copy()
matches_per_venue = data[['mid', 'venue']].drop_duplicates()
matches_count = matches_per_venue['venue'].value_counts()

plt.figure(figsize=(12,6))
sns.barplot(x=matches_count.values, y=matches_count.index,palette="rainbow")
plt.title('Number of Matches per Venue')
plt.xlabel('Number of Matches')
plt.ylabel('Venue')
plt.show()

Output:

venue — Matches per stadium

Next we will calculate the maximum runs scored by each batsman by grouping the data by batsman their runs. Then we’ll identify the top 10 batsmen with the highest runs and display this information using a horizontal bar chart.

Python

runs_by_batsman = data.groupby('batsman')['runs'].max().sort_values(ascending=False).head(10)

plt.figure(figsize=(12,6))
sns.barplot(x=runs_by_batsman.values, y=runs_by_batsman.index,palette="pastel")
plt.title('Top 10 Batsmen by Total Runs')
plt.xlabel('Total Runs')
plt.ylabel('Batsman')
plt.show()

Output:

batsman — max runs by batsmans

After that we can do the same for the bowlers, in terms of total wickets.

Python

wickets_by_bowler = data.groupby('bowler')['wickets'].max().sort_values(ascending=False).head(10)

plt.figure(figsize=(12,6))
sns.barplot(x=wickets_by_bowler.values, y=wickets_by_bowler.index, palette="muted")
plt.title('Top 10 Bowlers by Wickets Taken')
plt.xlabel('Total Wickets')
plt.ylabel('Bowler')
plt.show()

Output:

bowlers — Max wickets my bowlers

4. Performing Label Encoding

We will convert categorical text data into numeric labels using Label Encoding because ML models work with numbers.

LabelEncoder() converts text labels into integers.
fit_transform() learns encoding and applies it.
copy() : creates a duplicate of the DataFrame to avoid changing the original data

A dictionary assignment stores each encoder for future use like decoding or consistent transformation

Python

from sklearn.preprocessing import LabelEncoder

cat_cols = ['bat_team', 'bowl_team', 'venue', "batsman", "bowler"]

data_encoded = data.copy()

label_encoders = {}

for col in cat_cols:
    le = LabelEncoder()
    data_encoded[col] = le.fit_transform(data_encoded[col])
    label_encoders[col] = le

5. Performing Feature Selection

We drop date and mid columns because they are identifiers and don’t provide meaningful information for correlation analysis. By removing these irrelevant columns we focus on features that can reveal relationships useful for modeling or insights. Computing and visualizing correlations helps identify which features are related and can guide feature selection.

drop() : removes specified columns from the DataFrame
corr() : computes pairwise correlations between numerical features
sns.heatmap() : creates a colored matrix to visualize correlations with values
plt.show() : displays the plot on screen

Python

data_corr=data_encoded.drop(columns=["date","mid"],axis=1)
sns.heatmap(data_corr.corr(),annot=True)
plt.show()

Output:

corr — Performing Feature Selection

Based on the heatmap we will drop the columns runs_last_5, wickets_last_5 and non-striker because they show high correlation with other features. Dropping highly correlated columns helps reduce redundancy and prevents multicollinearity which can improve model performance and interpretability.

6. Splitting the Dataset into Training and Testing

We will select relevant features and the target variable then split the data into training and testing sets for model building and evaluation.

DataFrame indexing (data_encoded[feature_cols]) : selects specified columns as features
train_test_split() : splits features and target into training and test subsets
test_size=0.3 : assigns 30% of data for testing
random_state=42 : ensures reproducible splits by fixing the random seed

Python

feature_cols = ['bat_team', 'bowl_team', 'venue', 'runs', 'wickets', 'overs','striker','batsman','bowler']

X = data_encoded[feature_cols]
y = data_encoded['total'] 

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

7. Performing Feature Scaling

We will perform Min-Max scaling on our input features to ensure all the features are on the same scale. It ensures consistent scale and improves model performance. Scaling will be done on both training and testing data using the scaling parameters.

MinMaxScaler() scales features to [0,1] range.
fit_transform() fits scaler on training data and transforms it.
transform() applies same scaler to test data.

Python

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()

X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

8. Building the Neural Network

We will build neural network using TensorFlow and Keras for regression. After building the model we have compiled the model using the Huber Loss because of the robustness of the regression against outliers.

keras.Sequential() creates a stack of layers.
Dense layers are fully connected layers.
activation=relu adds non-linearity.
Output layer uses linear activation because it’s regression.
Huber loss combines MSE and MAE advantages to handle outliers better.
adam optimizer adjusts weights efficiently.

Python

model = keras.Sequential([
    keras.layers.Input( shape=(X_train_scaled.shape[1],)),
    keras.layers.Dense(512, activation='relu'),  
    keras.layers.Dense(216, activation='relu'),  
    keras.layers.Dense(1, activation='linear') 
])

huber_loss = tf.keras.losses.Huber(delta=1.0)  # You can adjust the 'delta' parameter as needed
model.compile(optimizer='adam', loss=huber_loss)

9. Training the Model

We train the model on the scaled training data for 10 epochs with a batch size of 64, validating on the test set.

model.fit() trains the model.
epochs=10 means the model sees the whole data 10 times.
batch_size=64 updates weights after every 64 samples.
validation_data evaluates model on test set during training.

Python

model.fit(X_train_scaled, y_train, epochs=10, batch_size=64, validation_data=(X_test_scaled, y_test))

Output:

training — Model Training

We can plot the loss and validation loss of the model.

Python

model_losses = pd.DataFrame(model.history.history)
model_losses.plot()

Output:

loss_vs_valoss — Loss VS Validation Loss

10. Evaluating the Model

We predict scores on test data and evaluate model performance using mean absolute error (MAE).

Python

predictions = model.predict(X_test_scaled)

from sklearn.metrics import mean_absolute_error,mean_squared_error
mean_absolute_error(y_test,predictions)

Output:

14.4113130569458

11. Creating an Interactive Widget for Score Prediction

We build an interactive interface using ipywidgets so users can select match details and get a live predicted score.

widgets.Dropdown() creates dropdown menus.
widgets.Button() creates a clickable button.
predict_score() function handles user inputs, encodes and scales them, runs prediction and displays result.
display() shows widgets in the notebook.

Python

import numpy as np
import ipywidgets as widgets
from IPython.display import display, clear_output
import warnings
warnings.filterwarnings("ignore")

venue = widgets.Dropdown(options=list(label_encoders['venue'].classes_), description='Select Venue:')
venue.style = {'description_width': 'initial'}

batting_team = widgets.Dropdown(options=list(label_encoders['bat_team'].classes_), description='Select Batting Team:')
batting_team.style = {'description_width': 'initial'}

bowling_team = widgets.Dropdown(options=list(label_encoders['bowl_team'].classes_), description='Select Bowling Team:')
bowling_team.style = {'description_width': 'initial'}

striker = widgets.Dropdown(options=list(label_encoders['batsman'].classes_), description='Select Striker:')
striker.style = {'description_width': 'initial'}

bowler = widgets.Dropdown(options=list(label_encoders['bowler'].classes_), description='Select Bowler:')
bowler.style = {'description_width': 'initial'}

runs = widgets.IntText(value=0, description='Runs:', style={'description_width': 'initial'})
wickets = widgets.IntText(value=0, description='Wickets:', style={'description_width': 'initial'})
overs = widgets.FloatText(value=0.0, description='Overs:', style={'description_width': 'initial'})
striker_ind = widgets.IntText(value=0, description='Striker:', style={'description_width': 'initial'})  # Assuming 0 or 1
predict_button = widgets.Button(description="Predict Score")

output = widgets.Output()

def predict_score(b):
    with output:
        clear_output()  # Clear previous output
        
        encoded_venue = label_encoders['venue'].transform([venue.value])[0]
        encoded_batting_team = label_encoders['bat_team'].transform([batting_team.value])[0]
        encoded_bowling_team = label_encoders['bowl_team'].transform([bowling_team.value])[0]
        encoded_striker = label_encoders['batsman'].transform([striker.value])[0]
        encoded_bowler = label_encoders['bowler'].transform([bowler.value])[0]
        
        input_features = [
            encoded_batting_team,
            encoded_bowling_team,
            encoded_venue,
            runs.value,
            wickets.value,
            overs.value,
            striker_ind.value,
            encoded_striker,
            encoded_bowler
        ]
    
        input_array = np.array(input_features).reshape(1, -1)
        input_array = scaler.transform(input_array)
        predicted_score = model.predict(input_array)
        
        
        print(f"Predicted Total Runs: {int(predicted_score[0])}")
        
predict_button.on_click(predict_score)

display(venue, batting_team, bowling_team, striker, bowler,
        runs, wickets, overs,
        striker_ind,
        predict_button, output)

Output:

output — Interactive Widget for Score Prediction

We have predicted the score of the match between CSK and RCB in Chinnaswamy Cricket Stadium. The predicted score of the match is 152.

You can download the source code from here.

Dogecoin Price Prediction with Machine Learning

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Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo

Lung Cancer Detection Using Transfer Learning

Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique

Pneumonia Detection using Deep Learning

In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta

Detecting Covid-19 with Chest X-ray

COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic

Skin Cancer Detection using TensorFlow

In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us

Age Detection using Deep Learning in OpenCV

The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'

Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV

In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff

Detecting COVID-19 From Chest X-Ray Images using CNN

A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T

Image Segmentation Using TensorFlow

Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,

License Plate Recognition with OpenCV and Tesseract OCR

License Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar

Detect and Recognize Car License Plate from a video in real time

Recognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro

Residual Networks (ResNet) - Deep Learning

After the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble

Natural Language Processing Projects

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Facebook Sentiment Analysis using python

This article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w

Next Sentence Prediction using BERT

Next Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows

Hate Speech Detection using Deep Learning

There must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat

Image Caption Generator using Deep Learning on Flickr8K dataset

Generating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image

Movie recommendation based on emotion in Python

Movies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their

Speech Recognition in Python using Google Speech API

Speech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, youâ€™ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require

Voice Assistant using python

Speech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide weâ€™ll create a basic voice assistant using Python.Step 1: Install Required

Human Activity Recognition - Using Deep Learning Model

Human activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque

Fine-tuning BERT model for Sentiment Analysis

Google created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train

Sentiment Classification Using BERT

BERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec

Sentiment Analysis with an Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews

Building an Autocorrector Using NLP in Python

Autocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N

Python | NLP analysis of Restaurant reviews

Natural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach

Restaurant Review Analysis Using NLP and SQLite

Normally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei

Twitter Sentiment Analysis using Python

This article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. Itâ€™s also known as opinion mini

Clustering Projects

Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning

Music Recommendation System Using Machine Learning

When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona

K means Clustering â€“ Introduction

K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ

Image Segmentation using K Means Clustering

Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.Â Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im

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