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Autism Prediction using Machine Learning
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Rainfall Prediction using Machine Learning - Python

Last Updated : 06 Sep, 2024
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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 dataset
First Five rows of the dataset

Now let's check the size of the dataset.

Python 
df.shape

Output:

(366, 12)

Let's check which column of the dataset contains which type of data.

Python 
df.info()

Output:

Information regarding data in the columns
Information regarding data in the columns

As per the above information regarding the data in each column, we can observe that there are no null values.

Python 
df.describe().T

Output:

Descriptive statistical measures of the dataset
Descriptive statistical measures of the dataset

Data 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 
df.isnull().sum()

Output:

Sum of null values present in each column
Sum of null values present in each column

So 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 
df.columns

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
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 data
Distribution plot for the columns with continuous data

Let'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
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 features
Heat map to detect highly correlated features

Now 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.

  • LogisticRegression
  • XGBClassifier
  • SVC
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 data
Confusion matrix for the validation data

Let'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        74

Get the complete notebook and dataset link here:

1. Notebook Link: click here.

2. Dataset Link: click here.


Next Article
Autism Prediction using Machine Learning

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Article Tags :
  • Machine Learning
  • AI-ML-DS
  • python
Practice Tags :
  • Machine Learning
  • python

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      In this tutorial, we will demonstrate how to build a dog breed classifier using transfer learning. This method allows us to use a pre-trained deep learning model and fine-tune it to classify images of different dog breeds. Why to use Transfer Learning for Dog Breed ClassificationTransfer learning is
      9 min read
    • Flower Recognition Using Convolutional Neural Network
      Convolutional Neural Network (CNN) are a type of deep learning model specifically designed for processing structured grid data such as images. In this article we will build a CNN model to classify different types of flowers from a dataset containing images of various flowers like roses, daisies, dan
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    • Emojify using Face Recognition with Machine Learning
      In this article, we will learn how to implement a modification app that will show an emoji of expression which resembles the expression on your face. This is a fun project based on computer vision in which we use an image classification model in reality to classify different expressions of a person.
      7 min read
    • Cat & Dog Classification using Convolutional Neural Network in Python
      Convolutional Neural Networks (CNNs) are a type of deep learning model specifically designed for processing images. Unlike traditional neural networks CNNs uses convolutional layers to automatically and efficiently extract features such as edges, textures and patterns from images. This makes them hi
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    • Traffic Signs Recognition using CNN and Keras in Python
      We always come across incidents of accidents where drivers' Overspeed or lack of vision leads to major accidents. In winter, the risk of road accidents has a 40-50% increase because of the traffic signs' lack of visibility. So here in this article, we will be implementing Traffic Sign recognition us
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    • Lung Cancer Detection using Convolutional Neural Network (CNN)
      Computer Vision is one of the applications of deep neural networks and one such use case is in predicting the presence of cancerous cells. In this article, we will learn how to build a classifier using Convolution Neural Network which can classify normal lung tissues from cancerous tissues.The follo
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    • Lung Cancer Detection Using Transfer Learning
      Computer Vision is one of the applications of deep neural networks that enables us to automate tasks that earlier required years of expertise and one such use in predicting the presence of cancerous cells.In this article, we will learn how to build a classifier using the Transfer Learning technique
      8 min read
    • Pneumonia Detection using Deep Learning
      In this article, we will discuss solving a medical problem i.e. Pneumonia which is a dangerous disease that may occur in one or both lungs usually caused by viruses, fungi or bacteria. We will detect this lung disease based on the x-rays we have. Chest X-rays dataset is taken from Kaggle which conta
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    • Detecting Covid-19 with Chest X-ray
      COVID-19 pandemic is one of the biggest challenges for the healthcare system right now. It is a respiratory disease that affects our lungs and can cause lasting damage to the lungs that led to symptoms such as difficulty in breathing and in some cases pneumonia and respiratory failure. In this artic
      9 min read
    • Skin Cancer Detection using TensorFlow
      In this article, we will learn how to implement a Skin Cancer Detection model using Tensorflow. We will use a dataset that contains images for the two categories that are malignant or benign. We will use the transfer learning technique to achieve better results in less amount of training. We will us
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    • Age Detection using Deep Learning in OpenCV
      The task of age prediction might sound simple at first but it's quite challenging in real-world applications. While predicting age is typically seen as a regression problem this approach faces many uncertainties like camera quality, brightness, climate condition, background, etc. In this article we'
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    • Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
      In this article, we will use mediapipe python library to detect face and hand landmarks. We will be using a Holistic model from mediapipe solutions to detect all the face and hand landmarks. We will be also seeing how we can access different landmarks of the face and hands which can be used for diff
      4 min read
    • Detecting COVID-19 From Chest X-Ray Images using CNN
      A Django Based Web Application built for the purpose of detecting the presence of COVID-19 from Chest X-Ray images with multiple machine learning models trained on pre-built architectures. Three different machine learning models were used to build this project namely Xception, ResNet50, and VGG16. T
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    • Image Segmentation Using TensorFlow
      Image segmentation refers to the task of annotating a single class to different groups of pixels. While the input is an image, the output is a mask that draws the region of the shape in that image. Image segmentation has wide applications in domains such as medical image analysis, self-driving cars,
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    • License Plate Recognition with OpenCV and Tesseract OCR
      License 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
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    • Detect and Recognize Car License Plate from a video in real time
      Recognizing 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 Learning
      After 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 Python
      This 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 python
      This 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
      6 min read
    • Next Sentence Prediction using BERT
      Next 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
      4 min read
    • Hate Speech Detection using Deep Learning
      There 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
      5 min read
    • Image Caption Generator using Deep Learning on Flickr8K dataset
      Generating 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
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    • Movie recommendation based on emotion in Python
      Movies 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
      4 min read
    • Speech Recognition in Python using Google Speech API
      Speech 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 python
      Speech 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 Model
      Human 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 Analysis
      Google 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 BERT
      BERT 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
      5 min read
    • Building an Autocorrector Using NLP in Python
      Autocorrect 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 reviews
      Natural 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 SQLite
      Normally, 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
      9 min read
    • Twitter Sentiment Analysis using Python
      This 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

    • Customer Segmentation using Unsupervised Machine Learning in Python
      Customer Segmentation involves grouping customers based on shared characteristics, behaviors and preferences. By segmenting customers, businesses can tailor their strategies and target specific groups more effectively and enhance overall market value. Today we will use Unsupervised Machine Learning
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    • Music Recommendation System Using Machine Learning
      When did we see a video on youtube let's say it was funny then the next time you open your youtube app you get recommendations of some funny videos in your feed ever thought about how? This is nothing but an application of Machine Learning using which recommender systems are built to provide persona
      4 min read
    • K means Clustering – Introduction
      K-Means Clustering is an Unsupervised Machine Learning algorithm which groups unlabeled dataset into different clusters. It is used to organize data into groups based on their similarity. Understanding K-means ClusteringFor example online store uses K-Means to group customers based on purchase frequ
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    • Image Segmentation using K Means Clustering
      Image segmentation is a technique in computer vision that divides an image into different segments. This can help identify specific objects, boundaries or patterns in the image.  Image is basically a set of given pixels and in image segmentation pixels with similar intensity are grouped together. Im
      2 min read

    Recommender System Project

    • AI Driven Snake Game using Deep Q Learning
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