In this project, We are going to predict Medical insurance costs. We implemented Random Forest Regression using Python. The data has been downloaded from Kaggle website (medical insurance cost dataset) The data contains following columns: sex: insurance contractor gender, female, male bmi: Body mass index, providing an understanding of body, weights that are relatively high or low relative to height, objective index of body weight (kg / m ^ 2) using the ratio of height …
In this project, we aim to implement Time Series Forecasting using Prophet The data contains the following columns: lat : String variable, Latitude lng: String variable, Longitude desc: String variable, Description of the Emergency Call zip: String variable, Zipcode title: String variable, Title timeStamp: String variable, YYYY-MM-DD HH:MM:SS twp: String variable, Township addr: String variable, Address e: String variable, Dummy variable (always 1) . let’s get our environment ready with the libraries we’ll need and …
In this project, we used the sales data of mobile phones in various companies. The aim of this project is to find out the relation between features of a mobile phone(eg:- RAM, Internal Memory, etc) and selling price. In addition, predict the price range of the mobile. We are going to use Kernel Support Vector Machine, K-Fold Cross Validation and Grid Search to solve this problem. The data contains the following columns: battery_power: Total energy …
In this project, we aim to predict the intention of online shoppers. We implemented the Logistic Regression to create our model and Linear Discriminant Analysis to create dimensionality reduction in the dataset using Python. The data contains the following columns: Administrative: Administrative Value Administrative_Duration: Duration in Administrative Page Informational: Informational Value Informational_Duration: Duration in Informational Page ProductRelated: Product Related Value ProductRelated_Duration: Duration in Product Related Page BounceRates: Bounce Rates of a web page ExitRates: Exit rate …
In this project, we are going to implement customer segmentation based on credit card usage behavior with two different approaches (K-means and Hierarchical Clustering) The data contains the following columns: CUST_ID : Identification of credit card holder (Categorical) BALANCE : Balance amount left in their account to make purchases BALANCE_FREQUENCY : How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated) PURCHASES : Amount of purchases made from account ONEOFF_PURCHASES : Maximum …
Most of the Restaurants ask reviews to the customers and based on the reviews the restaurant can improve the customer satisfaction. So Reviews plays a vital role for the successful growth of the restaurant. The aim of this project is to predict whether the review is positive or negative. This project implemented by Natural Language Processing and Naive Bayes on Python. The dataset consists of 1000 rows and 2 columns. Review Column consist of customer …
You have a supermarket mall and through membership cards, you have some basic data about your customers like Customer ID, age, gender, annual income and spending score. Spending Score is something you assign to the customer based on your defined parameters like customer behavior and purchasing data. By the end of this case study, you would be able to answer the below questions. 1- How to achieve customer segmentation using Machine Learning algorithm (Hierarchical Clustering) …
In this project, we aim to predict whether a tumor is benign or malignant. we implemented KNN on Python. The data contains the following columns: BI_RADS_assessment: Definitely benign(1) to Highly suggestive of malignancy (5) Age: patient’s age in years Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) Severity: Predictor Class: benign=0 or malignant=1 . let’s get our environment …