Nov 2, 2021

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:

https://www.kaggle.com/maajdl/yeh-concret-data

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:

Avocado Prices Dataset,

Bike Sharing Dataset,

Medical Cost Personal Dataset,


 

If you need implementation for any of the topics mentioned above or assignment help on any of its variants, feel free to contact us

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