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所在平台: Udemy |
课程主页: https://www.udemy.com/course/hands-on-car-price-prediction-data-analysis-model-building-deployment/
课程评论：没有评论
Coursera 课程 [数据科学：汽车价格预测 - 模型构建与部署] 课程总结 本课程是一项实践性项目，旨在教授学员如何利用机器学习模型，根据汽车的各项特征来预测其价格。课程将详细指导您完成从数据探索、分析、准备，到模型构建、评估，最终将模型部署到云平台，并通过用户界面让客户能够与之交互的整个流程。 课程将重点介绍以下内容： * **数据准备与分析**： * 安装必要的软件包和导入库。 * 加载数据源并进行全面的数据理解，包括数据清洗、单变量和双变量分析。 * 进行变量相关性分析（热力图）和可视化（散点图、数据分布图），并进行异常值分析。 * 将数值变量转换为分类变量（数据分箱）以及将分类特征转换为数值特征（独热编码）。 * 进行训练集和测试集划分，并使用 StandardScaler 对变量进行缩放。 * **模型构建与评估**： * 使用 XGBoost 回归算法构建价格预测模型。 * 通过 RandomizedSearchCV 进行超参数调优。 * 使用选定的超参数构建最终模型，并进行 R2 分数计算和预测值与实际值散点图的评估。 * 提取最重要的特征及其系数。 * **模型部署与用户界面**： * 介绍 Streamlit 及其安装步骤。 * 创建用户界面，以便与构建好的模型进行交互。 * 学习在本地计算机上运行 Notebook。 * 将项目推送到 GitHub 仓库。 * 在 Heroku 平台上免费部署项目。 本课程涵盖了数据分析、机器学习和模型部署等 21 世纪最受欢迎的技能。完成课程后，您将获得 AutomationGig 的结业证书，并可以访问课程中使用的数据集和 Jupyter Notebook。
This course is about predicting the price of a car based on its features using Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building and evaluation and deployment techniques. We will use XGBoost algorithm to create our model which helps us in predicting price of a car given its features.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Installing PackagesTask 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Data UnderstandingTask 5 : Data CleaningTask 6 : Performing Univariate analysis on variables.Task 7 : Performing Bivariate analysis on variables.Task 8 : Data binning to convert numerical variables to categorical variables.Task 9 : Finding correlations among features and plotting on HeatMap.Task 10: Plotting scatter plots.Task 11: Visualizing the distribution of data across variables.Task 12: Outlier Analysis.Task 13: Performing One Hot Encoding to convert categorical features to numeric features.Task 14: Train Test Split.Task 15: Scaling the variables using StandardScaler.Task 16: Creating a XGBoostRegression model with default parameters.Task 17: Hyperparameter Tuning using RandomizedSearchCV.Task 18: Building XGBRegression model with the selected hyperparameters.Task 19: Model Evaluation - Calculating R2 scoreTask 20: Model Evaluation - Plotting a scatter plot of the actual and predicted values.Task 21: Extracting most important features and its coefficients.Task 22: What is Streamlit and Installation steps.Task 23: Creating an user interface to interact with our created model.Task 24: How to run your notebook on Streamlit Server in your local machine.Task 25: Pushing your project to GitHub repository.Task 26: Project Deployment on Heroku Platform for free.Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. All the datasets used in the course are in the resources section.3. The Jupyter notebook are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course![Please note that this course and its related contents are for educational purpose only]Happy Learning!!
