Project: Predicting Temperature in London

Beta

As the climate changes, predicting the weather becomes ever more important for businesses. Since the weather depends on a lot of different factors, you will want to run a lot of experiments to determine what the best approach is to predict the weather. In this project, you will run experiments for different regression models predicting the mean temperature, using a combination of sklearn and MLflow.

You will be working with data stored in london_weather.csv, which contains the following columns:

date - recorded date of measurement - (int)
cloud_cover - cloud cover measurement in oktas - (float)
sunshine - sunshine measurement in hours (hrs) - (float)
global_radiation - irradiance measurement in Watt per square meter (W/m2) - (float)
max_temp - maximum temperature recorded in degrees Celsius (°C) - (float)
mean_temp - mean temperature in degrees Celsius (°C) - (float)
min_temp - minimum temperature recorded in degrees Celsius (°C) - (float)
precipitation - precipitation measurement in millimeters (mm) - (float)
pressure - pressure measurement in Pascals (Pa) - (float)
snow_depth - snow depth measurement in centimeters (cm) - (float)

import pandas as pd
import numpy as np
import mlflow
import mlflow.sklearn
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor

# Load data and perform exploratory analysis


def preprocess_df(df, feature_selection, target_var):
    """
    Split dataframe into X and y, and train and test consecutively. Then impute and scale both train and test features. Returns the train and test ets
    """
    # Complete this function
    
    return X_train, X_test, y_train, y_test

feature_selection = []
target_var = ''

X_train, X_test, y_train, y_test = preprocess_df(df, feature_selection, target_var)

def predict_and_evaluate(model, x_test, y_test):
    """
    Predict values from test set, calculate and return the root mean squared error.
    """
    # Complete this function
    
    return rmse

EXPERIMENT_NAME = ""
EXPERIMENT_ID = mlflow.create_experiment(EXPERIMENT_NAME)

# Adjust the parameters
max_depth_parameters = [1, 2]

for idx, depth in enumerate([1, 2, 5, 10, 20]):
    parameters = {
        'max_depth': depth,
        'random_state': 1
    }    
    RUN_NAME = f"run_{idx}"
    # Complete the experiment loop


experiment_results = mlflow.search_runs(experiment_names=[EXPERIMENT_NAME])
experiment_results