Lottery Prediction

Lottery Winner Prediction/Model/README.md

@@ -0,0 +1,33 @@
+# Lottery Prediction Model
+
+This repository contains the code for a lottery prediction model. The model is trained on a dataset downloaded from Kaggle and uses various machine learning algorithms such as RandomForestRegressor, LogisticRegression, Support Vector Regression (SVR), and TensorFlow.
+
+## Dataset
+
+The dataset used for training the model can be found on Kaggle. It has undergone exploratory data analysis (EDA) to understand its structure and characteristics.
+`https://www.kaggle.com/datasets/muhammadzain010/lottery-ticket-prediction-based-on-history`
+
+## Model Training
+
+The model is trained using the following algorithms:
+
+- RandomForestRegressor: This algorithm is used to build a regression model based on random decision trees.
+- LogisticRegression: This algorithm is used for binary classification tasks.
+- SVR: Support Vector Regression is used to build a regression model based on support vector machines.
+- TensorFlow: TensorFlow is used to build and train a deep learning model for lottery prediction.
+
+## Usage
+
+To use the lottery prediction model, follow these steps:
+
+1. Clone the repository to your local machine.
+   `git clone filepath`
+2. Install the required dependencies mentioned in the `requirements.txt` file.
+3. Run the appropriate script or notebook to train the model using the desired algorithm.
+4. Once the model is trained, you can use it to make predictions on new lottery data.
+
+## Conclusion
+
+<br /> There were some `obj` columns which were converted to `int`.
+<br /> For categorical feature the missing values were not large so can be replaced with most_frequent_ones or Median.
+<br /> The project concluded with the use of the different models to predict the lottery.

Lottery Winner Prediction/requirements.txt

@@ -0,0 +1,9 @@
+The python latest version needs to install (above 3.9)
+
+The library needs to install:-
+1. numpy 
+2. pandas
+3. matplotlib.pyplot 
+4. seaborn 
+
+The Jupiter notebook needs to install by using anaconda or vscode for a better view.