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In this repository, an attempt was made to practice the supervised machine learning techniques, in particular the classification based technique, i.e., Decision Trees and Random Forests. The csv sheet contains the data which is loaded in the jupyter notebook ("training.ipynb") from which further analysis was done.

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anshhagrawal/Decision_Tress_Random_Forest

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README.md
README.md
 
 
data_description.txt
data_description.txt
 
 
randforest.ipynb
randforest.ipynb
 
 
train.csv
train.csv
 
 
training.ipynb
training.ipynb
 
 
validation.ipynb
validation.ipynb
 
 

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Decision_Tress_Random_Forest

The data used in training and validation code files is fetched from "train.csv". The data is entirely fictional and any resemblance to an existing dataset is pure coincidence. Credits to DanB. (2018). Housing Prices Competition for Kaggle Learn Users. Kaggle. https://kaggle.com/competitions/home-data-for-ml-course.

The explanation for data in 'train.csv' is illustrated in "data_description.txt"

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In this repository, an attempt was made to practice the supervised machine learning techniques, in particular the classification based technique, i.e., Decision Trees and Random Forests. The csv sheet contains the data which is loaded in the jupyter notebook ("training.ipynb") from which further analysis was done.

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machine-learning random-forest decision-trees

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