Rainfall Prediction using Machine Learning - Python
Last Updated :
06 Sep, 2024
Today there are no certain methods by using which we can predict whether there will be rainfall today or not. Even the meteorological department's prediction fails sometimes. In this article, we will learn how to build a machine-learning model which can predict whether there will be rainfall today or not based on some atmospheric factors. This problem is related to Rainfall Prediction using Machine Learning because machine learning models tend to perform better on the previously known task which needed highly skilled individuals to do so.
Importing Libraries and Dataset
Python libraries make it easy for us to handle the data and perform typical and complex tasks with a single line of code.
- Pandas - This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
- Numpy - Numpy arrays are very fast and can perform large computations in a very short time.
- Matplotlib/Seaborn - This library is used to draw visualizations.
- Sklearn - This module contains multiple libraries are having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
- XGBoost - This contains the eXtreme Gradient Boosting machine learning algorithm which is one of the algorithms which helps us to achieve high accuracy on predictions.
- Imblearn - This module contains a function that can be used for handling problems related to data imbalance.
Python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.linear_model import LogisticRegression
from imblearn.over_sampling import RandomOverSampler
import warnings
warnings.filterwarnings('ignore')
DataSet Link
Now let's load the dataset into the panda's data frame and print its first five rows.
Python
df = pd.read_csv('Rainfall.csv')
df.head()
Output:
First Five rows of the datasetNow let's check the size of the dataset.
Python
Output:
(366, 12)
Let's check which column of the dataset contains which type of data.
Python
Output:
Information regarding data in the columnsAs per the above information regarding the data in each column, we can observe that there are no null values.
Python
Output:
Descriptive statistical measures of the datasetData Cleaning
The data which is obtained from the primary sources is termed the raw data and required a lot of preprocessing before we can derive any conclusions from it or do some modeling on it. Those preprocessing steps are known as data cleaning and it includes, outliers removal, null value imputation, and removing discrepancies of any sort in the data inputs.
Python
Output:
Sum of null values present in each columnSo there is one null value in the 'winddirection' as well as the 'windspeed' column. But what's up with the column name wind direction?
Python
Output:
Index(['day', 'pressure ', 'maxtemp', 'temperature', 'mintemp', 'dewpoint',
'humidity ', 'cloud ', 'rainfall', 'sunshine', ' winddirection',
'windspeed'],
dtype='object')Here we can observe that there are unnecessary spaces in the names of the columns let's remove that.
Python
df.rename(str.strip,
axis='columns',
inplace=True)
df.columns
Output:
Index(['day', 'pressure', 'maxtemp', 'temperature', 'mintemp', 'dewpoint',
'humidity', 'cloud', 'rainfall', 'sunshine', 'winddirection',
'windspeed'],
dtype='object')Now it's time for null value imputation.
Python
for col in df.columns:
# Checking if the column contains
# any null values
if df[col].isnull().sum() > 0:
val = df[col].mean()
df[col] = df[col].fillna(val)
df.isnull().sum().sum()
Output:
0
Exploratory Data Analysis
EDA is an approach to analyzing the data using visual techniques. It is used to discover trends, and patterns, or to check assumptions with the help of statistical summaries and graphical representations. Here we will see how to check the data imbalance and skewness of the data.
Python
plt.pie(df['rainfall'].value_counts().values,
labels = df['rainfall'].value_counts().index,
autopct='%1.1f%%')
plt.show()
Output:
Pie chart for the number of data for each target
Python
df.groupby('rainfall').mean()
Output:
Here we can clearly draw some observations:
- maxtemp is relatively lower on days of rainfall.
- dewpoint value is higher on days of rainfall.
- humidity is high on the days when rainfall is expected.
- Obviously, clouds must be there for rainfall.
- sunshine is also less on days of rainfall.
- windspeed is higher on days of rainfall.
The observations we have drawn from the above dataset are very much similar to what is observed in real life as well.
Python
features = list(df.select_dtypes(include = np.number).columns)
features.remove('day')
print(features)
Output:
['pressure', 'maxtemp', 'temperature', 'mintemp', 'dewpoint', 'humidity', 'cloud', 'sunshine', 'winddirection', 'windspeed']
Let's check the distribution of the continuous features given in the dataset.
Python
plt.subplots(figsize=(15,8))
for i, col in enumerate(features):
plt.subplot(3,4, i + 1)
sb.distplot(df[col])
plt.tight_layout()
plt.show()
Output:
Distribution plot for the columns with continuous dataLet's draw boxplots for the continuous variable to detect the outliers present in the data.
Python
plt.subplots(figsize=(15,8))
for i, col in enumerate(features):
plt.subplot(3,4, i + 1)
sb.boxplot(df[col])
plt.tight_layout()
plt.show()
Output:
Box plots for the columns with continuous data There are outliers in the data but sadly we do not have much data so, we cannot remove this.
Python
df.replace({'yes':1, 'no':0}, inplace=True)
Sometimes there are highly correlated features that just increase the dimensionality of the feature space and do not good for the model's performance. So we must check whether there are highly correlated features in this dataset or not.
Python
plt.figure(figsize=(10,10))
sb.heatmap(df.corr() > 0.8,
annot=True,
cbar=False)
plt.show()
Output:
Heat map to detect highly correlated featuresNow we will remove the highly correlated features 'maxtemp' and 'mintemp'. But why not temp or dewpoint? This is because temp and dewpoint provide distinct information regarding the weather and atmospheric conditions.
Python
df.drop(['maxtemp', 'mintemp'], axis=1, inplace=True)
Model Training
Now we will separate the features and target variables and split them into training and testing data by using which we will select the model which is performing best on the validation data.
Python
features = df.drop(['day', 'rainfall'], axis=1)
target = df.rainfall
As we found earlier that the dataset we were using was imbalanced so, we will have to balance the training data before feeding it to the model.
Python
X_train, X_val, \
Y_train, Y_val = train_test_split(features,
target,
test_size=0.2,
stratify=target,
random_state=2)
# As the data was highly imbalanced we will
# balance it by adding repetitive rows of minority class.
ros = RandomOverSampler(sampling_strategy='minority',
random_state=22)
X, Y = ros.fit_resample(X_train, Y_train)
The features of the dataset were at different scales so, normalizing it before training will help us to obtain optimum results faster along with stable training.
Python
# Normalizing the features for stable and fast training.
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_val = scaler.transform(X_val)
Now let's train some state-of-the-art models for classification and train them on our training data.
Python
models = [LogisticRegression(), XGBClassifier(), SVC(kernel='rbf', probability=True)]
for i in range(3):
models[i].fit(X, Y)
print(f'{models[i]} : ')
train_preds = models[i].predict_proba(X)
print('Training Accuracy : ', metrics.roc_auc_score(Y, train_preds[:,1]))
val_preds = models[i].predict_proba(X_val)
print('Validation Accuracy : ', metrics.roc_auc_score(Y_val, val_preds[:,1]))
print()
Output:
LogisticRegression() :
Training Accuracy : 0.8893967324057472
Validation Accuracy : 0.8966666666666667
XGBClassifier() :
Training Accuracy : 0.9903285270573975
Validation Accuracy : 0.8408333333333333
SVC(probability=True) :
Training Accuracy : 0.9026413474407211
Validation Accuracy : 0.8858333333333333
Model Evaluation
From the above accuracies, we can say that Logistic Regression and support vector classifier are satisfactory as the gap between the training and the validation accuracy is low. Let's plot the confusion matrix as well for the validation data using the SVC model.
Python
import matplotlib.pyplot as plt
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn import metrics
ConfusionMatrixDisplay.from_estimator(models[2], X_val, Y_val)
plt.show()
# This code is modified by Susobhan Akhuli
Output:
Confusion matrix for the validation dataLet's plot the classification report as well for the validation data using the SVC model.
Python
print(metrics.classification_report(Y_val,
models[2].predict(X_val)))
Output:
precision recall f1-score support
0 0.84 0.67 0.74 24
1 0.85 0.94 0.90 50
accuracy 0.85 74
macro avg 0.85 0.80 0.82 74
weighted avg 0.85 0.85 0.85 74Get the complete notebook and dataset link here:
1. Notebook Link: click here.
2. Dataset Link: click here.
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License Plate Recognition with OpenCV and Tesseract OCRLicense Plate Recognition is widely used for automated identification of vehicle registration plates for security purpose and law enforcement. By combining computer vision techniques with Optical Character Recognition (OCR) we can extract license plate numbers from images enabling applications in ar
5 min read
Detect and Recognize Car License Plate from a video in real timeRecognizing a Car License Plate is a very important task for a camera surveillance-based security system. We can extract the license plate from an image using some computer vision techniques and then we can use Optical Character Recognition to recognize the license number. Here I will guide you thro
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Residual Networks (ResNet) - Deep LearningAfter the first CNN-based architecture (AlexNet) that win the ImageNet 2012 competition, Every subsequent winning architecture uses more layers in a deep neural network to reduce the error rate. This works for less number of layers, but when we increase the number of layers, there is a common proble
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Natural Language Processing Projects
Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Facebook Sentiment Analysis using pythonThis article is a Facebook sentiment analysis using Vader, nowadays many government institutions and companies need to know their customers' feedback and comment on social media such as Facebook. What is sentiment analysis? Sentiment analysis is one of the best modern branches of machine learning, w
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Next Sentence Prediction using BERTNext Sentence Prediction is a pre-training task used in BERT to help the model understand the relationship between different sentences. It is widely used for tasks like question answering, summarization and dialogue systems. The goal is to determine whether a given second sentence logically follows
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Hate Speech Detection using Deep LearningThere must be times when you have come across some social media post whose main aim is to spread hate and controversies or use abusive language on social media platforms. As the post consists of textual information to filter out such Hate Speeches NLP comes in handy. This is one of the main applicat
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Image Caption Generator using Deep Learning on Flickr8K datasetGenerating a caption for a given image is a challenging problem in the deep learning domain. In this article we will use different computer vision and NLP techniques to recognize the context of an image and describe them in a natural language like English. We will build a working model of the image
12 min read
Movie recommendation based on emotion in PythonMovies that effectively portray and explore emotions resonate deeply with audiences because they tap into our own emotional experiences and vulnerabilities. A well-crafted emotional movie can evoke empathy, understanding, and self-reflection, allowing viewers to connect with the characters and their
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Speech Recognition in Python using Google Speech APISpeech recognition means converting spoken words into text. It used in various artificial intelligence applications such as home automation, speech to text, etc. In this article, you’ll learn how to do basic speech recognition in Python using the Google Speech Recognition API.Step 1: Install Require
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Voice Assistant using pythonSpeech recognition is the process of turning spoken words into text. It is a key part of any voice assistant. In Python the SpeechRecognition module helps us do this by capturing audio and converting it to text. In this guide we’ll create a basic voice assistant using Python.Step 1: Install Required
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Human Activity Recognition - Using Deep Learning ModelHuman activity recognition using smartphone sensors like accelerometer is one of the hectic topics of research. HAR is one of the time series classification problem. In this project various machine learning and deep learning models have been worked out to get the best final result. In the same seque
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Fine-tuning BERT model for Sentiment AnalysisGoogle created a transformer-based machine learning approach for natural language processing pre-training called Bidirectional Encoder Representations from Transformers. It has a huge number of parameters, hence training it on a small dataset would lead to overfitting. This is why we use a pre-train
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Sentiment Classification Using BERTBERT stands for Bidirectional Representation for Transformers and was proposed by researchers at Google AI language in 2018. Although the main aim of that was to improve the understanding of the meaning of queries related to Google Search, BERT becomes one of the most important and complete architec
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Sentiment Analysis with an Recurrent Neural Networks (RNN)Recurrent Neural Networks (RNNs) are used in sequence tasks such as sentiment analysis due to their ability to capture context from sequential data. In this article we will be apply RNNs to analyze the sentiment of customer reviews from Swiggy food delivery platform. The goal is to classify reviews
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Building an Autocorrector Using NLP in PythonAutocorrect feature predicts and correct misspelled words, it helps to save time invested in the editing of articles, emails and reports. This feature is added many websites and social media platforms to ensure easy typing. In this tutorial we will build a Python-based autocorrection feature using N
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Python | NLP analysis of Restaurant reviewsNatural language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to process and analyze large amounts of natural language data. It is the branch of mach
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Restaurant Review Analysis Using NLP and SQLiteNormally, a lot of businesses are remained as failures due to lack of profit, lack of proper improvement measures. Mostly, restaurant owners face a lot of difficulties to improve their productivity. This project really helps those who want to increase their productivity, which in turn increases thei
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Twitter Sentiment Analysis using PythonThis article covers the sentiment analysis of any topic by parsing the tweets fetched from Twitter using Python. What is sentiment analysis? Sentiment Analysis is the process of 'computationally' determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mini
10 min read
Clustering Projects
Recommender System Project