5 Beginner Data Analysis projects that help you check all the boxes✅
🏅 Data visualization ( Tableau / BI )
🏅 Data Cleaning ( SQL / Excel )
🏅 EDA ( Python / R )
🏅 Web Scraping ( Python )
🏅 Sentiment Analysis ( Python / R )
3. Text analysis
This is done through text features analysis like wordcloud and N-gram analysis.
To conclude, EDA is important before building models using Algorithms. First, comprehend the data deeply.
#EDA#DataScience#MachineLearning#dataanalysis
Mostly this EDA is performed using visual methods.
I will split these EDA into these categories.
1. Volumetric Analysis
2.Time Pattern Analysis
3.Text Analysis
4/n
8 focused steps with your dataset to make EDA...
1. View top records, how is the data.
2. Find categories and its distributions.
3. With numerical data: try statistics, visualize.
4,5. Imbalance dataset? Is it missing?
6,7. Remove Outliers, Duplicates
8. check Data relations.
Are you able to recognise if your Machine Learning model is telling you BS or not?
There's only one way: Exploratory Data Analysis (EDA) aka "get to know your data"
Some stuff you could be doing with your data 🤹♀️
A thread 🧶 [↓]
Are you able to recognise if your Machine Learning model is telling you BS or not?
There's only one way: Exploratory Data Analysis (EDA) aka "get to know your data"
Some stuff you could be doing with your data 🤹♀️
A thread 🧶 [↓]
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