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Estimation of Soil Shear
Strength using Machine Learning
language-python Copy code
# Importing the necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
import matplotlib.pyplot as plt
# Loading the dataset
data = pd.read_csv('soil_data.csv')
# Splitting the dataset into input features and target variable
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X = data[['plastic_limit', 'liquid_limit', 'specific_gravity', 'water_conten
y = data['shear_strength']
# Splitting the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_s
# Creating the Random Forest Regressor model
model = RandomForestRegressor()
# Training the model
model.fit(X_train, y_train)
# Predicting the soil shear strength
y_pred = model.predict(X_test)
# Plotting the actual and predicted soil shear strength
plt.scatter(y_test, y_pred)
plt.xlabel('Actual Soil Shear Strength')
plt.ylabel('Predicted Soil Shear Strength')
plt.title('Actual vs Predicted Soil Shear Strength')
plt.show()
In the code above, we are estimating the soil shear strength using
machine learning techniques. We have a dataset with four input
features: plastic limit, liquid limit, specific gravity, and water
content. Our goal is to predict the soil shear strength based on
these input features.
We start by importing the necessary libraries, including pandas
for data manipulation, sklearn for machine learning algorithms,
and matplotlib for data visualization.
Next, we load the dataset from a CSV file using the pd.read_csv()
function. The dataset contains the input features in columns
'plastic_limit', 'liquid_limit', 'specific_gravity', and
'water_content', and the target variable 'shear_strength'.
We then split the dataset into input features (X) and the target
variable (y) using the train_test_split() function. This function
splits the data into training and testing sets, with 80% of the
data used for training and 20% for testing.
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After that, we create a Random Forest Regressor model using the
RandomForestRegressor() class. This model is a type of ensemble
learning algorithm that combines multiple decision trees to make
predictions.
We train the model using the training data by calling the fit()
method on the model object. This step involves finding the optimal
parameters for the random forest algorithm.
Once the model is trained, we use it to predict the soil shear
strength for the testing data by calling the predict() method on the
model object. The predicted values are stored in the variable
y_pred.
Finally, we plot a scatter plot to visualize the relationship
between the actual and predicted soil shear strength. The x-axis
represents the actual soil shear strength, while the y-axis
represents the predicted soil shear strength. This plot helps us
evaluate the performance of our model.
Note: Make sure to replace 'soil_data.csv' with the actual
filename and path of your dataset.
By analyzing the scatter plot, we can observe how well our model
predicts the soil shear strength based on the given input
features. The closer the points are to the diagonal line, the
better the predictions are.
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