Project: Predicting Temperature in London

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
df = pd.read_csv('london_weather.csv', parse_dates=['date'])

df.head()

df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month

df.head()

df.shape

df.dtypes

df.info()

df.describe()

df[df.isnull().any(axis=1)]

df_year_temp_mean = df.groupby('year').agg({'max_temp':'mean', 'min_temp':'mean'})

df_year_temp_mean

sns.lineplot(data=df_year_temp_mean, x='year', y='min_temp')

sns.lineplot(data=df_year_temp_mean, x='year', y='max_temp')

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