# Month: August 2019

## Adult​ Census Income Analysis and Prediction

In this project, We aim to Predict whether income exceeds \$50K/yr based on census data. The data has been downloaded from the UCI Repository website (Adult). We implemented the Artificial Neural Network (ANN) on Python to solve this problem. The data contains the following culumns: Age: continuous.  Workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.  fnlwgt: continuous.  Education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.  Education-num: …

## Prediction of Medical Insurance Cost

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 …

## Prediction of Google Stocks Price

In this project, we are going to predict the stocks price of Alphabet Inc. The data contains the stocks price of Google from 2010 to 2019. This project will be implemented by Recurrent Neural Network and LSTM using Python. The data contains the following columns: Date Open High Low Close Volume . let’s get our environment ready with the libraries we’ll need and then import the data! Check out the Data Let’s extract the Open …

## Time Series Forecasting for 911 Calls

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 …

## Prediction of Aliens Vs. Predators

In this project, we will have some images of aliens and predators and by them, we will train a Convolutional Neural Network using Keras to predict if the image is an alien or predator. The Data contains the following folders:  train: 247 aliens and 247 predators validation: 100 aliens and 100 predators . Building CNN Let’s import the Keras libraries and packages. In this stage, we will initialise the CNN. Step 1: Create Convolutional Layer …

## Prediction of Spam Messages

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 …

## Prediction of Mobile Price

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 …

## Prediction of Online Shopper’s Intention

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 …