- 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

Cement (component 1) : quantitative -- kg in a m3 mixture -- Input Variable
Blast Furnace Slag (component 2): quantitative -- kg in a m3 mixture -- Input Variable
Fly Ash (component 3): quantitative -- kg in a m3 mixture -- Input Variable
Water (component 4): quantitative -- kg in a m3 mixture -- Input Variable
Superplasticizer (component 5): quantitative -- kg in a m3 mixture -- Input Variable
Coarse Aggregate (component 6): quantitative -- kg in a m3 mixture -- Input Variable
Fine Aggregate (component 7): quantitative -- kg in a m3 mixture -- Input Variable
Age: quantitative -- Day (1~365) -- Input Variable
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()