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Concrete Strength Dataset - Regression




Description :


This dataset provides information about the compressive strength of concrete which is the most important material in civil engineering based on its components and its age.



Recommended Model :


Algorithms to be used: Regression, SVM, RandomForestRegressor etc.


Recommended Project :

Prediction of concrete compressive strength



Dataset link:




Overview of data


Detailed overview of dataset:


- Rows = 1030

- Columns= 9


Name -- Data Type -- Measurement -- Description

  1. Cement (component 1) : quantitative -- kg in a m3 mixture -- Input Variable

  2. Blast Furnace Slag (component 2): quantitative -- kg in a m3 mixture -- Input Variable

  3. Fly Ash (component 3): quantitative -- kg in a m3 mixture -- Input Variable

  4. Water (component 4): quantitative -- kg in a m3 mixture -- Input Variable

  5. Superplasticizer (component 5): quantitative -- kg in a m3 mixture -- Input Variable

  6. Coarse Aggregate (component 6): quantitative -- kg in a m3 mixture -- Input Variable

  7. Fine Aggregate (component 7): quantitative -- kg in a m3 mixture -- Input Variable

  8. Age: quantitative -- Day (1~365) -- Input Variable

  9. Concrete compressive strength: quantitative -- MPa -- Output Variable



EDA [CODE]


import pandas as pd  
# load data data = pd.read_csv('Concrete_Data_Yeh.csv') 
data.head()

# check details of the dataframe 
data.info()










# check the no.of missing values in each column 
data.isna().sum()







# statistical information about the dataset 
data.describe()

# data distribution  

import seaborn as sns 
import matplotlib.pyplot as plt


sns.histplot(data['cement'], kde=False)
plt.show()

sns.histplot(data['slag'], kde=False)
plt.show()

sns.histplot(data['flyash'], kde=False)
plt.show()

sns.histplot(data['water'], kde=False)
plt.show()

sns.histplot(data['superplasticizer'], kde=False)
plt.show()

sns.histplot(data['coarseaggregate'], kde=False)
plt.show()

sns.histplot(data['fineaggregate'], kde=False)
plt.show()

sns.histplot(data['age'], kde=False)
plt.show()

sns.histplot(data['csMPa'], kde=False)
plt.show()




Other datasets for classification:




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