I have a dataframe with 76 columns. 1st column contains date values and the other 75 columns are groundwater levels form 75 different boreholes. I want to cluster the boreholes based on the trend (boreholes that follow the same pattern are grouped together). How can I do this in python?
Here is a sample of my dataframe
df = pd.DataFrame({
'Date': [1980, 1985, 1990, 1995, 2000],
'borehole1': [53, 43, 33, 22, 18],
'borehole2': [50, 40, 30, 50, 40],
'borehole3': [22, 32, 42, 32, 13],
'borehole4': [60, 65, 82, 72, 60],
'borehole5': [80, 70, 60, 80, 70],
'borehole6': [43, 33, 22, 18, 13]
})
df.plot()
In this example I would therefore have 3 clusters:
The K-Means algo is perfect for this! Here is a sample (below). Just point the X and y to your specific dataset and set the 'K' to 3 (already done for you in this example).
# K-MEANS CLUSTERING
# Importing Modules
from sklearn import datasets
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
# Loading dataset
iris_df = datasets.load_iris()
# Declaring Model
model = KMeans(n_clusters=3)
# Fitting Model
model.fit(iris_df.data)
# Predicitng a single input
predicted_label = model.predict([[7.2, 3.5, 0.8, 1.6]])
# Prediction on the entire data
all_predictions = model.predict(iris_df.data)
# Printing Predictions
print(predicted_label)
print(all_predictions)
# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :3] # we only take the first two features.
y = iris.target
fig = plt.figure(figsize=(10,10))
plt = fig.add_subplot(111, projection='3d')
plt.scatter(X[:,0],X[:,1],X[:,2],
c=all_predictions, edgecolor='red', s=40, alpha = 0.5)
plt.set_title("First three PCA directions")
plt.set_xlabel("Educational_Degree")
plt.set_ylabel("Gross_Monthly_Salary")
plt.set_zlabel("Claim_Rate")
plt.dist = 10
plt
See this link for more info.
https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html