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While thinking about how I could start this blog, I was thinking about one thing: this series isn’t for everyone. And that’s exactly what Hollywood needs to do more of. It needs to stop making things ‘for everybody.’ This series is absolutely bonkers with violence, explicit scenes, and episodes. From minds of Tim Miller and David Fincher made a show with great animation of a mix with Twilight Zone and Black Mirror storytelling. Credit must
right-to-work
In this project, we are going to analysis the right to work in different state. This dataset intends to compare certain statistics between RTW and non-RTW states. Namely, union membership, poverty rate, and median household income. The data has been collected from kaggle for downloading the dataset you can click the blow link: https://www.kaggle.com/datasets/brandonconrady/right-to-work-dataset?select=RTW_Membership.csv let’s get our environment ready with the libraries we’ll need and then import the data! import numpy as np import pandas
A novel strain of coronavirus — SARS-CoV-2 — was first detected in December 2019 in Wuhan, a city in China’s Hubei province with a population of 11 million, after an outbreak of pneumonia without an obvious cause. The virus has now spread to over 200 countries and territories across the globe, and was characterised as a pandemic by the World Health Organization (WHO) Let’s get our environment ready with the libraries we’ll need and then
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

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