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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
prediction of income
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: continuous. Marital-status: Married-civ-spouse, Divorced, Never-married,
machine learning insurance medical
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 to
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: DateOpenHighLowCloseVolume . let’s get our environment ready with the libraries we’ll need and then import the data! import numpy as np import matplotlib.pyplot as plt import pandas as pd import
In this project, we aim to implement Time Series Forecasting using Prophet The data contains the following columns: lat : String variable, Latitudelng: String variable, Longitudedesc: String variable, Description of the Emergency Callzip: String variable, Zipcodetitle: String variable, TitletimeStamp: String variable, YYYY-MM-DD HH:MM:SStwp: String variable, Townshipaddr: String variable, Addresse: String variable, Dummy variable (always 1) . let’s get our environment ready with the libraries we’ll need and then import the data! import numpy as np
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 predatorsvalidation: 100 aliens and 100 predators . Building CNN Let’s import the Keras libraries and packages. from keras.models import Sequential from keras.layers import Convolution2D from keras.layers import MaxPooling2D from keras.layers
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 messageCategory: Spam or Ham . let’s get our environment ready with the libraries we’ll need and then import the data! import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns %matplotlib inline plt.style.use('seaborn-deep') from sklearn.metrics import confusion_matrix
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