patilpankajsudhakar/diamond_price_end_to_end_project

Machine Learning live class project in @ineuron.ai

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End To End ML Project

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End to End ML Project

Project Name - Diamond Price Prediction

e8410de1166e37977556a607623f0194f9812f73

created a environment

conda create -p venv python==3.8

conda activate venv/

Install all necessary libraries

pip install -r requirements.txt

Documentation

Diamond Price Prediction End to End project is a data science project that aims to build a machine learning model to predict the price of diamonds. The project involves collecting, cleaning, and preprocessing a dataset of diamond characteristics and corresponding prices. 

The dataset typically includes various features such as carat weight, cut quality, color, clarity, depth, table, symmetry, and polish. The target variable is the price of the diamond. The dataset can be obtained from various sources such as online diamond retailers, diamond trading platforms, or publicly available datasets.

The project involves several stages, including data cleaning and preprocessing, exploratory data analysis, feature engineering, model selection, hyperparameter tuning, and model evaluation. 

The data cleaning and preprocessing stage involve handling missing values, dealing with outliers, and transforming variables to ensure they are suitable for the machine learning model.

The exploratory data analysis stage involves visualizing the dataset and understanding the relationships between the features and target variable. This stage can help to identify any patterns or correlations that can be used to improve the machine learning model.

The feature engineering stage involves creating new features from the existing ones that may improve the performance of the machine learning model. This stage can include feature scaling, one-hot encoding, and creating interaction terms.

The model selection stage involves choosing the best machine-learning algorithm for the task. This stage can involve comparing the performance of several algorithms such as linear regression, decision trees, random forests, and gradient boosting.

The hyperparameter tuning stage involves optimizing the parameters of the machine learning model to achieve the best performance. This stage can involve techniques such as grid search or random search.

The model evaluation stage involves assessing the performance of the machine learning model on a holdout dataset. This stage can involve metrics such as mean squared error, root mean squared error, and R-squared.

The final output of the Diamond Price Prediction End-to-End project is a machine learning model that can predict the price of diamonds based on their characteristics. This model can be deployed in a production environment, such as a website or mobile application, to assist in diamond pricing and trading.
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