• A python project to deal with air-passengers per month prediction over 2 decades (1949-1960).
• It utilizes LSTM to predict total air-passengers/month after feauture selection.
• It has over 260+ rows and 2 columns for Time-series forecasting.
• The Data visualization shows an almost accurate prediction

• Clone the repo in local Github.
• Use Google Colab or Install Jupyter
• Run the .ipnyb file and ensure to give correct path for CSV
• Now import dataset airline-passengers.csv from already available datasets in Google Colaboratory
• If using Jupyter/Anaconda download dataset from:https://www.kaggle.com/datasets/rakannimer/airline-passengers
• Ensure dataset is present in same directory and specify the correct path

• This project performs data preprocessing like One Hot Encoding, CHI square test
• It further performs cross validation and uses best result for accurate prediction of Breast Cancer
• Artificial Neural Networks is used as base Deep Learning algorithm
• 95.3% Accuracy is achieved with ANN
• A heatmap is also depicted for visualizing prediction

• Clone the repo in local Github.
• For .py file install latest version from python official website Python or upgrade to Python3
• Now download dataset from this repository and upload it to Google Colaboratory or set appropriate path in Jupyter
• If using Jupyter/Anaconda download dataset from: https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data
• Install all libraries in terminal/Command Prompt (using cmd command on Windows)

  pip install numpy
  

   pip install pandas
  

  pip install matplotlib
  

  pip install seaborn
  

  pip install tensorflow
  

  pip install sklearn
  

• Ensure dataset is present in same directory and specify the correct path

This project is licensed under MIT License