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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
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Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
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8 Ways to Improve Accuracy of Machine Learning Models
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Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Classification of Crops using Machine Learning!

Priyal
Priyal Last Updated : 02 Jun, 2021
5 min read

This article was published as a part of the Data Science Blogathon

Introduction

Today, we have Agriculture data set with us for better understanding and we’ll be focused on how to think about these projects, rather than their implementation as many of us getting trouble in initiating and doing the ending of projects.

In this article, we do simple classification modeling and trying to get good accuracy.

You can download the dataset from here.

Aim :

To determine the outcome of the harvest season, i.e. whether the crop would be healthy (alive), damaged by pesticides, or damaged by other reasons.

Data Description:

We have two datasets given to train and test.

ID: UniqueID Estimated_Insects_Count: Estimated insects count per square meter Crop_Type: Category of Crop(0,1) Soil_Type: Category of Soil (0,1) Pesticide_Use_Category: Type of pesticides uses (1- Never, 2-Previously Used, 3-Currently Using) Number_Doses_Week: Number of doses per week Number_Weeks_Used: Number of weeks used Number_Weeks_Quit: Number of weeks quit Season: Season Category (1,2,3) Crop_Damage: Crop Damage Category (0=alive, 1=Damage due to other causes, 2=Damage due to Pesticides)

1. Importing Libraries and Dataset

The first step is importing the necessary libraries like NumPy, pandas, matplotlib, and seaborn in our notebook.

Then we move on to load the dataset from CSV format and convert it into panda DataFrame and check the top five rows to analyze the data.

classification | dataset

2. Cleaning Dataset

1. Checking Null Values: By using dataset isnull().sum() we check that there were 9000 missing values present in the dataset i.e. in the Number_Weeks_Used variable.

2. Checking Datatypes: We checked Datatypes of all columns, to see any inconsistencies in the data.

3. Checking Unique Values: Now we have to understand unique values if present in columns, which will help to reduce dimensionality in future processing.

4. Replacing missing values: As there are 9000 missing values in the Number_Weeks_Used column so we can replace them by mode of the data. And again if we check null values we saw that there are no null values present in our dataset.

3. Exploratory Data Analysis :

In this part, we will be conducting EDA to gain insights into the data and get familiar with it.

First, we will get information by using info() method.

classification | info

 

  • Correlation: 

 Checking correlation with sns.heatmap() .

corelation | classification

Inferences drawn from heatmap:

1.Estimated_Insects_count,Pesticide_use_category and Number_weeks_used are positively correlated with Crop damage.
2.Number_weeks_used is positively correlated with Estimated_Insects_count and Pesticide_use_category.
3.Number_weeks_Quit is highly negatively correlated with Pesticide_use_category and Number_weeks_used.

  • Data Visualization:

For gathering insights, data visualization is a must.

a. Univariate Analysis:

For univariate, I plotted countplot of Crop_Damage.

crop type

Observations:

  1. Crop damage due to pesticides is less in comparison to damage due to other causes.
  2. Crop type 0 has a higher chance of survival compared to crop type 1.

Now I plotted countplot for Crop_Damage vs Insect count for Crop Type.

count plot crop damage

Observations:

        1.Type 2 pesticide is much safer to use as compared to Type 3 pesticide.

        2.Type 3 pesticide shows most pesticide-related damage to crops.

Another plot in univariate analysis for gathering more insights.

distribution plot
 

Observations:

        1.From Graph 1 we can conclude that till 20-25 weeks damage due to pesticide is negligible.
        2. From Graph 3 we can see that after 20 weeks damage due to the use of pesticide increases significantly.

 

b. Bivariate Analysis: 

Plotted barplot between Crop_Damage vs Estimated_Insects_Count.

barplot

Clearly observed from the above plot that Most insect attacks are done on crop type 0.

Barplot between Crop_Type vs Number_Weeks_Used.

 

crop type vs no. of weeks used

Observations:

1.Crop Type 0 is more vulnerable to pesticide-related and other damages as compared to Type1.

2.Avg. duration of pesticide-related damage is lower for Crop type 1.

4. Data Pre-processing :

  • Outliers Analysis : 

Now we will check for outliers using Boxplot. 

boxplot

Clearly, some outliers are present in Insect_Count, doses_week, and number_weeks_quit columns.
Now for removing these outliers I simply find the mean value of each column and replace it with an outlier value.

After removing outliers,

classification | after outlier removal

  • Skew Analysis : 

Checking skewness of our data using histplot and observed that all the data is normally distributed.

Now, our dataset is ready to be put into the machine learning model for classification analysis.

 

5. Building Machine Learning Model:

Scaling Dataset:

As usual, the first step is to drop the target variable and then scaling the dataset by using Standard Scaler to make the data normally distributed.

Splitting Dataset:

After preprocessing, we now split the data into training/testing subsets.

Evaluating Models:

We now checked various classification models and calculated metrics such as the precision, recall, and F1 score.

The models we will run here are:

  1. Random Forest
  2. K Nearest Neighbor(KNN)
  3. Decision Tree Classifier
  4. Gaussian NB
classification | cross validation

From initial model accuracy values, we see that KNN is performing better than others with 83% accuracy. It has a maximum accuracy score and minimum standard deviations.

Now I find best parameter i.e. n_neighbors using GridSearchCV taking ‘n_neighbors’ range = (2,30) , cv = 5 and  scoring = ‘accuracy’ for our KNN model and found n_neighbor = 22.

Again running the KNN model with its best parameters i.e. n_neighbor = 22.

Result :

classification | knn

To check model performance, we will now plot different performance metrics.

a. Confusion Matrix:

 

confusion matrix

b. Precision and Recall :

 

classification | precision and recall

 

From observation, we found decent accuracy ( ~0.84), precision, and recall for the model. This indicates that the model is a good fit for the prediction.

For better results, one can do hyperparameter tuning which will help in increasing the accuracy of the model.

But overall KNN gives good accuracy among all the models so we save it as our final model.

For your reference you can check my complete solution and dataset used  from this link:

https://github.com/priyalagarwal27/Crop-Detection-and-Prediction-using-Classification-

Hope this would be helpful for you. Please share what’s your results and suggestions on this down below in the comment section. Hope you liked the article as well. For more reading check out my other articles also:

https://github.com/priyalagarwal27/Regression-on-ELECTION-dataset

https://github.com/priyalagarwal27/Classification—SLOAN-Space-Dataset

Thanks for reading !!! Cheers!

About the author:

Priyal Agarwal

Please feel free to contact me on Linkedin, Email.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.

Priyal
Priyal

Hello everyone out there. I'm Priyal Agarwal, working as a Data Analyst. With a background in data science and analytics, I’m passionate about leveraging data to drive business strategies and enhance customer experiences. I’m particularly interested in predictive analytics and machine learning.
I am excited about contributing to the data science community by developing innovative solutions that push the boundaries of what's possible. I believe that data science and AI have the power to revolutionize industries and improve lives, and I am eager to be at the forefront of this transformative journey. Looking forward to connecting with like-minded professionals and expanding my knowledge.

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ashok kr sharma
ashok kr sharma

Hello Priya, Did you use satellite imagery for classifying the crops? The categorization of crops and estimation are essential parameters. Let me know.

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