In this project, We aim to Predict the number of future road accidents in the UK. The data has been published from the Department for Transport (GB)(Road Accident). This data provides detailed road safety data about the circumstances of personal injury road accidents in GB from 2014 to 2017. We are going to implement Time Series Forecasting using ARIMA & Prophet to find out the number of road accidents in the future. . What is Time …
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 aim to predict whether the message is spam or ham. we implemented Natural Language Processing, TF-IDF and SVM on Python. The data contains the following columns: Message: text message Category: Spam or Ham . let’s get our environment ready with the libraries we’ll need and then import the data! Check out the Data . Exploratory Data Analysis Let’s use describe by Category, this way we can begin to think about the …
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 …