sachinsatya / diamond_price_prediction-python

The aim of this analysis is to predict the price of diamonds based on their characteristics. The dataset used for this analysis is the Diamonds dataset from Kaggle.

Objective: The main goal of this analysis is to develop a predictive model that can estimate the price of diamonds based on their characteristics.

Dataset Source: The Diamonds dataset used in this analysis is obtained from Kaggle, a popular platform for data science and machine learning.

Dataset Description: The Diamonds dataset contains various features or attributes of diamonds, such as carat weight, cut quality, color, clarity, depth, table, and dimensions, along with their corresponding prices.

Exploratory Data Analysis (EDA): Before building the predictive model, an exploratory data analysis will be performed to understand the distribution, relationships, and patterns within the dataset. This may involve visualizations, summary statistics, and data preprocessing steps.

Feature Selection and Engineering: Based on the insights gained from the EDA, relevant features will be selected for model training. Additionally, new features may be engineered from the existing ones to improve the predictive power of the model.

Model Selection: Different regression models, such as linear regression, decision trees, random forests, or gradient boosting, will be considered for predicting diamond prices. The choice of the model will depend on the nature of the data and the desired level of accuracy.

Model Training and Evaluation: The selected model will be trained using a portion of the dataset and evaluated using appropriate metrics such as mean squared error (MSE), root mean squared error (RMSE), or R-squared score to assess its performance.

Hyperparameter Tuning: If applicable, hyperparameter tuning techniques like grid search or random search will be employed to optimize the model's performance by finding the best combination of hyperparameters.

Model Validation: To ensure the model's generalizability, it will be validated using a separate test dataset that was not used during training. This step helps estimate how well the model will perform on unseen data.

Model Deployment and Predictions: Once the model is trained, it can be deployed to make predictions on new, unseen diamond data. These predictions can assist in estimating diamond prices for future transactions or market analysis.

Model Interpretation: The final model will be analyzed to understand the impact of each feature on the predicted diamond prices. This analysis helps gain insights into the factors that contribute most significantly to the pricing of diamonds.