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Machine Learning Learning from course I did and sample data analysis done with ML

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ml_learnings's Introduction

ML Projects for learning

Below are some ML projects I was working on during my learning ML and here is the summary.

This project focused on predicting house prices in the Boston metropolitan area based on historical data using regression models. The following are key aspects of this project:

  1. Problem and Objective: The City of Boston has launched an initiative to identify under-valued properties and increase their value for residents. To achieve this, we need a model that can effectively predict house prices in the Boston metropolitan area.
  2. Methods: Regression models were applied for more accurate predictions.
  3. Data Preprocessing Steps: Handling missing values, outliers, inconsistencies, and handling skewness were employed.
  4. Results: Train various regression models and identify which is the more performant model.
  5. Dataset Details: The dataset includes 506 cases with features such as crime rate, average number of rooms, population density, etc.

This project aimed to predict used car prices based on various attributes like year, kilometers driven, mileage, engine power, seats, and previous owner data using regression models. The following are key aspects of this project:

  1. Problem and Objective: Cars4u needs a more accurate and reliable way to estimate the value of used cars for pricing purposes.
  2. Methods: Regression models were applied for more accurate predictions, with data preprocessing steps such as handling missing values, outliers, inconsistencies, skewness, and populating data based on car names.
  3. Results: Train various regression models and identify which is the more performant model for more accurate predictions.
  4. Dataset Details: The dataset includes 6018 instances with features such as year, kilometers driven, mileage, engine power, seats, new_price, price previous owned, etc.

By using regression models on both projects, we aim to predict prices and develop profitable pricing strategies for different applications.

License

MIT License

Copyright (c) 2023 Elephanta Technologies and Design Inc (elephantatech) (vivek mistry)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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