NAME - PRANAV BHAWANE BATCH - D83 DOMAIN - DATA SCIENCE

Industrial-copper-modeling

The copper industry deals with less complex data related to sales and pricing. However, this data may suffer from issues such as skewness and noisy data, which can affect the accuracy of manual predictions. Dealing with these challenges manually can be time-consuming and may not result in optimal pricing decisions. A machine learning regression model can address these issues by utilizing advanced techniques such as data normalization, feature scaling, and outlier detection, and leveraging algorithms that are robust to skewed and noisy data. Another area where the copper industry faces challenges is in capturing the leads. A lead classification model is a system for evaluating and classifying leads based on how likely they are to become a customer . You can use the STATUS variable with WON being considered as Success and LOST being considered as Failure and remove data points other than WON, LOST STATUS values.

The learning outcomes of this project are:

1. Developing proficiency in Python programming language and its data analysis libraries such as Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, and Streamlit.

2. Gaining experience in data preprocessing techniques such as handling missing values, outlier detection, and data normalization to prepare data for machine learning modeling.

3. Understanding and visualizing the data using EDA techniques such as boxplots, histograms, and scatter plots.

4. Learning and applying advanced machine learning techniques such as regression and classification to predict continuous and binary target variables, respectively.

5. Building and optimizing machine learning models using appropriate evaluation metrics and techniques such as cross-validation and grid search.

6. Experience in feature engineering techniques to create new informative representations of the data.

7. Developing a web application using the Streamlit module to showcase the machine learning models and make predictions on new data.

8. Understanding the challenges and best practices in the manufacturing domain and how machine learning can help solve them.