DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHON

Name: DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHON
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SKU: 00858967

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862190DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHONhttps://www.gandhi.com.mx/data-science-for-sales-analysis-forecasting-clustering-and-prediction-with-python/phttps://gandhi.vtexassets.com/arquivos/ids/1290829/e1382cb5-78ae-434c-ba06-585a765c0631.jpg?v=638337799568500000224224MXNBALIGE PUBLISHINGInStock/Ebooks/858967DATA SCIENCE FOR SALES ANALYSIS, FORECASTING, CLUSTERING, AND PREDICTION WITH PYTHON224224https://www.gandhi.com.mx/data-science-for-sales-analysis-forecasting-clustering-and-prediction-with-python/phttps://gandhi.vtexassets.com/arquivos/ids/1290829/e1382cb5-78ae-434c-ba06-585a765c0631.jpg?v=638337799568500000InStockMXN99999DIEbook20221230005570195_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1230005570195_The dataset used in this project is from Walmart which is a renowned retail corporation that operates a chain of hypermarkets. Walmart has provided a data combining of 45 stores including store information and monthly sales. The data is provided on weekly basis. Walmart tries to find the impact of holidays on the sales of store. For which it has included four holidays weeks into the dataset which are Christmas, Thanksgiving, Super bowl, Labor Day. In this project, you are going to analyze, forecast weekly sales, perform clustering, and predict the resulting clusters.The dataset covers sales from 2010-02-05 to 2012-11-01. Following are the attributes in the dataset: Store - the store number; Date - the week of sales; Weekly_Sales - sales for the given store; Holiday_Flag - whether the week is a special holiday week 1 Holiday week 0 Non-holiday week; Temperature - Temperature on the day of sale; Fuel_Price - Cost of fuel in the region; CPI Prevailing consumer price index; and Unemployment - Prevailing unemployment rate.To perform regression on weekly sales, you will use: Linear Regression, Random Forest regression, Decision Tree regression, Support Vector Machine regression, Nave Bayes regression, K-Nearest Neighbor regression, Adaboost regression, Gradient Boosting regression, Extreme Gradient Boosting regression, Light Gradient Boosting regression, Catboost regression, and MLP regression. To perform clustering, you will use K-Means algorithm.The machine learning models used predict clusters as target variable are K-Nearest Neighbor classifier, Random Forest classifier, Naive Bayes classifier, Logistic Regression classifier, Decision Tree classifier, Support Vector Machine classifier, LGBM classifier, Gradient Boosting classifier, XGB classifier, and MLP classifier. Finally, you will plot boundary decision, distribution of features, feature importance, cross validation score, and predicted values versus true values, confusion matrix, learning curve, performance of the model, scalability of the model, training loss, and training accuracy.(*_*)1230005570195_In this comprehensive data science project focusing on sales analysis, forecasting, clustering, and prediction with Python, we embarked on an enlightening journey of data exploration and analysis. Our primary objective was to gain valuable insights from the dataset and leverage the power of machine learning to make accurate predictions and informed decisions.We began by meticulously exploring the dataset, examining its structure, and identifying any missing or inconsistent data. By visualizing features distributions and conducting statistical analyses, we gained a better understanding of the datas characteristics and potential challenges.The first key aspect of the project was weekly sales forecasting. We employed various machine learning regression models, including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, Gradient Boosting Regression, Extreme Gradient Boosting Regression, Light Gradient Boosting Regression, KNN Regression, Catboost Regression, Nave Bayes Regression, and Multi-Layer Perceptron Regression. These models enabled us to predict weekly sales based on relevant features, allowing us to uncover patterns and relationships between different factors and sales performance. To optimize the performance of our regression models, we employed grid search with cross-validation. This technique systematically explored hyperparameter combinations to find the optimal configuration, maximizing the models accuracy and predictive capabilities.Moving on to data segmentation, we adopted the widely-used K-means clustering technique, an unsupervised learning method. The goal was to divide data into distinct segments. By determining the optimal number of clusters through grid search with cross-validation, we ensured that the clustering accurately captured the underlying patterns in the data.The next phase of the project focused on predicting the cluster of new customers using machine learning classifiers. We employed powerful classifiers such as Logistic Regression, K-Nearest Neighbors, Support Vector, Decision Trees, Random Forests, Gradient Boosting, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP) to make accurate predictions. Grid search with cross-validation was again applied to fine-tune the classifiers hyperparameters, enhancing their performance.Throughout the project, we emphasized the significance of feature scaling techniques, such as Min-Max scaling and Standardization. These preprocessing steps played a crucial role in ensuring that all features were on the same scale, contributing equally during model training, and improving the models interpretability.Evaluation of our models was conducted using various metrics. For regression tasks, we utilized mean squared error, while classification tasks employed accuracy, precision, recall, and F1-score. The use of cross-validation helped validate the models robustness, providing comprehensive assessments of their effectiveness.Visualization played a vital role in presenting our findings effectively. Utilizing libraries such as Matplotlib and Seaborn, we created informative visualizations that facilitated the communication of complex insights to stakeholders and decision-makers.Throughout the project, we followed an iterative approach, refining our strategies through data preprocessing, model training, and hyperparameter tuning. The grid search technique proved to be an invaluable tool in identifying the best parameter combinations, resulting in more accurate predictions and meaningful customer segmentation.In conclusion, this data science project demonstrated the power of machine learning techniques in sales analysis, forecasting, and customer segmentation. The insights and recommendations generated from the models can provide valuable guidance for businesses seeking to optimize sales strategies, target marketing efforts, and make data-driven decisions to achieve growth and success. The project showcases the importance of leveraging advanced analytical methods to unlock hidden patterns and unleash the full potential of data for business success...1230005570195_BALIGE PUBLISHINGlibro_electonico_21254bbc-b9c1-373c-8136-baa11b6ce332_1230005570195;1230005570195_1230005570195Rismon HasiholanInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/f2ca928b-958d-498f-bb70-f76c6615d8f6-epub-27d749dc-7495-4ba6-ae93-86dc1e576e41.epub2022-05-14T00:00:00+00:00BALIGE PUBLISHING