Photo by Joshua Hoehne on Unsplash

Real-time tweets about the covid-19 vaccine have been analyzed in this article. My analyzes give answers to whether people think positive or negative about the covid-19 vaccine, or are neutral. You will see which words are tweeted the most with the covid-19 vaccine. Also, tweets about the types of vaccines were compared.

I got the tweets with my Twitter developer account and used Python.

The Data

I started with store authentication credentials in relevant variables. I limited the last 1000 tweets.

I filtered tweets to track only these keywords; ‘covid19 vaccine’, ‘covid-19 vaccine’, ‘coronavirus vaccine’, ‘covid19 vaccines’, ‘covid-19 vaccines’, ‘coronavirus vaccines’…

Photo by ev on Unsplash

In this post, the goal is to build a prediction model using Simple Linear Regression and Random Forest in Python. The dataset is available on Kaggle and my codes on my Github account.

Let’s get started to understand the dataset.

Discover and Visualize Data

In this dataset, there are 3 categorical features Email, Address, and Avatar; 5 numeric features Avg. Session Length, Time on App, Time on Website, Length of Membership, and Yearly Amount Spent. And there are 500 instances. That is small but perfect for beginners.

Hello everyone! I did some Exploratory Data Analysis and Visualizations for the US YouTube Trending Videos dataset using Python.

The codes will be above the relevant descriptions for all my steps. Let’s see what I found!

from google.colab import files
uploaded = files.upload()
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plot
df = pd.read_csv(‘US_youtube_trending_data.csv’)

I started by uploading the dataset and importing libraries. The dataset is available on Kaggle. Then the data frame has been created. You can see it’s first 3 rows in Figure 1. The dataset has…

Hello everyone! Here is my analysis for Spotify Dataset.

I will explain my analysis in Exploratory Data Analysis approach of the Spotify Dataset using Python. The dataset is available on Kaggle. By the way, I used Google Colab.

First, I imported the csv file in Google Colab which is shown below.

from google.colab import files
uploaded = files.upload()

I’ve created “df” function which represents my data frame. Data frame helps to see datasets arrange in rows and columns. The first variable was unnamed and the column showed just row number then I removed it. These steps are shown below.


Ayça Erbaşı

Interested in MachineLearning | DataScience. Industrial Engineer.

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