R&R (Ratings and Reviews) vs Sales Analysis

Pandas: For data manipulation and merging sales data with ratings and reviews. Example: pandas.merge() to combine datasets.

NumPy: For numerical computations and handling large arrays of data. Example: Performing calculations to analyze sales trends.

Matplotlib/Seaborn: For data visualization to identify trends and correlations between ratings, reviews, and sales. Example: Creating plots to visualize the relationship between average ratings and sales performance.

Scikit-learn: For statistical analysis and modeling to determine the impact of ratings and reviews on sales. Example: Using regression models to assess relationships.

Statsmodels:For advanced statistical analysis, hypothesis testing, and regression modeling. Example: statsmodels.api.OLS() for ordinary least squares regression to analyze the impact of ratings and reviews on sales.

SciPy: For performing statistical tests and optimizations. Example: Using scipy.stats to conduct correlation analysis.

SQLAlchemy: For querying and managing data from relational databases if sales and reviews data is stored in a database. Example: Efficiently retrieving and processing large datasets.

LightGBM or XGBoost: For machine learning models to predict sales based on various features, including ratings and reviews. Example: Implementing gradient boosting models for regression analysis.

Explanation:

Clustering based on different hipotesis of Average ratings to identify the best sales, or where the sales increase.

Clustering (scikit-learn library): 

- K-means: general purpose, even cluster size, not too many clusters, inductive (distances between points)

   The KMeans algorithm clusters data by trying to separate samples in n groups of equal variance, minimizing a criterion known as the inertia or within-cluster sum-of-squares (see below). This algorithm requires the number of clusters to be specified.

- Mini Batch K-Means

   The MiniBatchKMeans is a variant of the KMeans algorithm which uses mini-batches to reduce the computation time, while still attempting to optimise the same objective function. Mini-batches are subsets of the input data, randomly sampled in each training iteration. These mini-batches drastically reduce the amount of computation required to converge to a local solution.

- DBSCAN: non-flat geometry, unevent cluster sizes, outlier removal, transductive (distances between nearest points)

   The DBSCAN algorithm views clusters as areas of high density separated by areas of low density. Due to this rather generic view, clusters found by DBSCAN can be any shape, as opposed to k-means which assumes that clusters are convex shaped.

- Hierarchical clustering: Many clusters, possibly connectivity constraints, transductive (Distances between points)

    Hierarchical clustering is a general family of clustering algorithms that build nested clusters by merging or splitting them successively.

Different techniques: 

Non-flat geometry clustering is useful when the clusters have a specific shape, i.e. a non-flat manifold, and the standard euclidean distance is not the right metric. This case arises in the two top rows of the figure above.

Gaussian mixture models, useful for clustering, are described in another chapter of the documentation dedicated to mixture models. KMeans can be seen as a special case of Gaussian mixture model with equal covariance per component.

Transductive clustering methods (in contrast to inductive clustering methods) are not designed to be applied to new, unseen data.