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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

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.

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 :

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



 Checking correlation with sns.heatmap() .

Inferences drawn from heatmap:

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 damage due to pesticides is less in comparison to damage due to other causes.

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.


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

        chúng tôi 3 pesticide shows most pesticide-related damage to crops.

Another plot in univariate analysis for gathering more insights.


        chúng tôi 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.

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

Barplot between Crop_Type vs Number_Weeks_Used.


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. 

Now for removing these outliers I simply find the mean value of each column and replace it with an outlier value.

After removing outliers,

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:

Random Forest

K Nearest Neighbor(KNN)

Decision Tree Classifier

Gaussian NB

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 :

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

a. Confusion Matrix:


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:

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.


You're reading Classification Of Crops Using Machine Learning!

Stock Prices Prediction Using Machine Learning And Deep Learning

17 minutes


Rating: 5 out of 5.


Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. psychological, rational and irrational behavior, etc. All these aspects combine to make share prices volatile and very difficult to predict with a high degree of accuracy.

Can we use machine learning as a game-changer in this domain? Using features like the latest announcements about an organization, their quarterly revenue results, etc., machine learning techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions.

The core idea behind this article is to showcase how these algorithms are implemented. I will briefly describe the technique and provide relevant links to brush up on the concepts as and when necessary. In case you’re a newcomer to the world of time series, I suggest going through the following articles first:

Are you a beginner looking for a place to start your data science journey? Presenting a comprehensive course, full of knowledge and data science learning, curated just for you! This course covers everything from basics of Machine Learning to Advanced concepts of ML, Deep Learning and Time series.

Understanding the Problem Statement

We’ll dive into the implementation part of this article soon, but first it’s important to establish what we’re aiming to solve. Broadly, stock market analysis is divided into two parts – Fundamental Analysis and Technical Analysis.

Fundamental Analysis involves analyzing the company’s future profitability on the basis of its current business environment and financial performance.

Technical Analysis, on the other hand, includes reading the charts and using statistical figures to identify the trends in the stock market.

As you might have guessed, our focus will be on the technical analysis part. We’ll be using a dataset from Quandl (you can find historical data for various stocks here) and for this particular project, I have used the data for ‘Tata Global Beverages’. Time to dive in!

Note: Here is the dataset I used for the code: Download

We will first load the dataset and define the target variable for the problem:

Python Code:

There are multiple variables in the dataset – date, open, high, low, last, close, total_trade_quantity, and turnover.

The columns Open and Close represent the starting and final price at which the stock is traded on a particular day.

High, Low and Last represent the maximum, minimum, and last price of the share for the day.

Total Trade Quantity is the number of shares bought or sold in the day and Turnover (Lacs) is the turnover of the particular company on a given date.

Another important thing to note is that the market is closed on weekends and public holidays. Notice the above table again, some date values are missing – 2/10/2023, 6/10/2023, 7/10/2023. Of these dates, 2nd is a national holiday while 6th and 7th fall on a weekend.

The profit or loss calculation is usually determined by the closing price of a stock for the day, hence we will consider the closing price as the target variable. Let’s plot the target variable to understand how it’s shaping up in our data:

#setting index as date df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d') df.index = df['Date'] #plot plt.figure(figsize=(16,8)) plt.plot(df['Close'], label='Close Price history')

In the upcoming sections, we will explore these variables and use different techniques to predict the daily closing price of the stock.

Moving Average Introduction

‘Average’ is easily one of the most common things we use in our day-to-day lives. For instance, calculating the average marks to determine overall performance, or finding the average temperature of the past few days to get an idea about today’s temperature – these all are routine tasks we do on a regular basis. So this is a good starting point to use on our dataset for making predictions.

The predicted closing price for each day will be the average of a set of previously observed values. Instead of using the simple average, we will be using the moving average technique which uses the latest set of values for each prediction. In other words, for each subsequent step, the predicted values are taken into consideration while removing the oldest observed value from the set. Here is a simple figure that will help you understand this with more clarity.

We will implement this technique on our dataset. The first step is to create a dataframe that contains only the Date and Close price columns, then split it into train and validation sets to verify our predictions.


Just checking the RMSE does not help us in understanding how the model performed. Let’s visualize this to get a more intuitive understanding. So here is a plot of the predicted values along with the actual values.

#plot valid['Predictions'] = 0 valid['Predictions'] = preds plt.plot(train['Close']) plt.plot(valid[['Close', 'Predictions']]) Inference

The RMSE value is close to 105 but the results are not very promising (as you can gather from the plot). The predicted values are of the same range as the observed values in the train set (there is an increasing trend initially and then a slow decrease).

In the next section, we will look at two commonly used machine learning techniques – Linear Regression and kNN, and see how they perform on our stock market data.

Linear Regression Introduction

The most basic machine learning algorithm that can be implemented on this data is linear regression. The linear regression model returns an equation that determines the relationship between the independent variables and the dependent variable.

The equation for linear regression can be written as:

Here, x1, x2,….xn represent the independent variables while the coefficients θ1, θ2, …. θn  represent the weights. You can refer to the following article to study linear regression in more detail:

For our problem statement, we do not have a set of independent variables. We have only the dates instead. Let us use the date column to extract features like – day, month, year,  mon/fri etc. and then fit a linear regression model.


We will first sort the dataset in ascending order and then create a separate dataset so that any new feature created does not affect the original data.

#setting index as date values df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d') df.index = df['Date'] #sorting data = df.sort_index(ascending=True, axis=0) #creating a separate dataset new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close']) for i in range(0,len(data)): new_data['Date'][i] = data['Date'][i] new_data['Close'][i] = data['Close'][i] #create features from fastai.structured import add_datepart add_datepart(new_data, 'Date') new_data.drop('Elapsed', axis=1, inplace=True)  #elapsed will be the time stamp

This creates features such as:

‘Year’, ‘Month’, ‘Week’, ‘Day’, ‘Dayofweek’, ‘Dayofyear’, ‘Is_month_end’, ‘Is_month_start’, ‘Is_quarter_end’, ‘Is_quarter_start’,  ‘Is_year_end’, and  ‘Is_year_start’.

Note: I have used add_datepart from fastai library. If you do not have it installed, you can simply use the command pip install fastai. Otherwise, you can create these feature using simple for loops in python. I have shown an example below.

Apart from this, we can add our own set of features that we believe would be relevant for the predictions. For instance, my hypothesis is that the first and last days of the week could potentially affect the closing price of the stock far more than the other days. So I have created a feature that identifies whether a given day is Monday/Friday or Tuesday/Wednesday/Thursday. This can be done using the following lines of code:

new_data['mon_fri'] = 0 for i in range(0,len(new_data)): if (new_data['Dayofweek'][i] == 0 or new_data['Dayofweek'][i] == 4):     new_data['mon_fri'][i] = 1 else:     new_data['mon_fri'][i] = 0

We will now split the data into train and validation sets to check the performance of the model.

#split into train and validation train = new_data[:987] valid = new_data[987:] x_train = train.drop('Close', axis=1) y_train = train['Close'] x_valid = valid.drop('Close', axis=1) y_valid = valid['Close'] #implement linear regression from sklearn.linear_model import LinearRegression model = LinearRegression(),y_train) Results #make predictions and find the rmse preds = model.predict(x_valid) rms=np.sqrt(np.mean(np.power((np.array(y_valid)-np.array(preds)),2))) rms 121.16291596523156

The RMSE value is higher than the previous technique, which clearly shows that linear regression has performed poorly. Let’s look at the plot and understand why linear regression has not done well:

#plot valid['Predictions'] = 0 valid['Predictions'] = preds valid.index = new_data[987:].index train.index = new_data[:987].index plt.plot(train['Close']) plt.plot(valid[['Close', 'Predictions']]) Inference

As seen from the plot above, for January 2023 and January 2023, there was a drop in the stock price. The model has predicted the same for January 2023. A linear regression technique can perform well for problems such as Big Mart sales where the independent features are useful for determining the target value.

k-Nearest Neighbours Introduction

Another interesting ML algorithm that one can use here is kNN (k nearest neighbours). Based on the independent variables, kNN finds the similarity between new data points and old data points. Let me explain this with a simple example.

Consider the height and age for 11 people. On the basis of given features (‘Age’ and ‘Height’), the table can be represented in a graphical format as shown below:

To determine the weight for ID #11, kNN considers the weight of the nearest neighbors of this ID. The weight of ID #11 is predicted to be the average of it’s neighbors. If we consider three neighbours (k=3) for now, the weight for ID#11 would be = (77+72+60)/3 = 69.66 kg.

For a detailed understanding of kNN, you can refer to the following articles:

Implementation #importing libraries from sklearn import neighbors from sklearn.model_selection import GridSearchCV from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler(feature_range=(0, 1))

Using the same train and validation set from the last section:

#scaling data x_train_scaled = scaler.fit_transform(x_train) x_train = pd.DataFrame(x_train_scaled) x_valid_scaled = scaler.fit_transform(x_valid) x_valid = pd.DataFrame(x_valid_scaled) #using gridsearch to find the best parameter params = {'n_neighbors':[2,3,4,5,6,7,8,9]} knn = neighbors.KNeighborsRegressor() model = GridSearchCV(knn, params, cv=5) #fit the model and make predictions,y_train) preds = model.predict(x_valid) Results #rmse rms=np.sqrt(np.mean(np.power((np.array(y_valid)-np.array(preds)),2))) rms 115.17086550026721

There is not a huge difference in the RMSE value, but a plot for the predicted and actual values should provide a more clear understanding.

#plot valid['Predictions'] = 0 valid['Predictions'] = preds plt.plot(valid[['Close', 'Predictions']]) plt.plot(train['Close']) Inference

The RMSE value is almost similar to the linear regression model and the plot shows the same pattern. Like linear regression, kNN also identified a drop in January 2023 since that has been the pattern for the past years. We can safely say that regression algorithms have not performed well on this dataset.

Let’s go ahead and look at some time series forecasting techniques to find out how they perform when faced with this stock prices prediction challenge.

Auto ARIMA Introduction

ARIMA is a very popular statistical method for time series forecasting. ARIMA models take into account the past values to predict the future values. There are three important parameters in ARIMA:

p (past values used for forecasting the next value)

q (past forecast errors used to predict the future values)

d (order of differencing)

Parameter tuning for ARIMA consumes a lot of time. So we will use auto ARIMA which automatically selects the best combination of (p,q,d) that provides the least error. To read more about how auto ARIMA works, refer to this article:

Implementation from pyramid.arima import auto_arima data = df.sort_index(ascending=True, axis=0) train = data[:987] valid = data[987:] training = train['Close'] validation = valid['Close'] model = auto_arima(training, start_p=1, start_q=1,max_p=3, max_q=3, m=12,start_P=0, seasonal=True,d=1, D=1, trace=True,error_action='ignore',suppress_warnings=True) forecast = model.predict(n_periods=248) forecast = pd.DataFrame(forecast,index = valid.index,columns=['Prediction']) Results rms=np.sqrt(np.mean(np.power((np.array(valid['Close'])-np.array(forecast['Prediction'])),2))) rms 44.954584993246954 #plot plt.plot(train['Close']) plt.plot(valid['Close']) plt.plot(forecast['Prediction']) Inference

As we saw earlier, an auto ARIMA model uses past data to understand the pattern in the time series. Using these values, the model captured an increasing trend in the series. Although the predictions using this technique are far better than that of the previously implemented machine learning models, these predictions are still not close to the real values.

As its evident from the plot, the model has captured a trend in the series, but does not focus on the seasonal part. In the next section, we will implement a time series model that takes both trend and seasonality of a series into account.

Prophet Introduction

There are a number of time series techniques that can be implemented on the stock prediction dataset, but most of these techniques require a lot of data preprocessing before fitting the model. Prophet, designed and pioneered by Facebook, is a time series forecasting library that requires no data preprocessing and is extremely simple to implement. The input for Prophet is a dataframe with two columns: date and target (ds and y).

Prophet tries to capture the seasonality in the past data and works well when the dataset is large. Here is an interesting article that explains Prophet in a simple and intuitive manner:

Implementation #importing prophet from fbprophet import Prophet #creating dataframe new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close']) for i in range(0,len(data)): new_data['Date'][i] = data['Date'][i] new_data['Close'][i] = data['Close'][i] new_data['Date'] = pd.to_datetime(new_data.Date,format='%Y-%m-%d') new_data.index = new_data['Date'] #preparing data new_data.rename(columns={'Close': 'y', 'Date': 'ds'}, inplace=True) #train and validation train = new_data[:987] valid = new_data[987:] #fit the model model = Prophet() #predictions close_prices = model.make_future_dataframe(periods=len(valid)) forecast = model.predict(close_prices) Results #rmse forecast_valid = forecast['yhat'][987:] rms=np.sqrt(np.mean(np.power((np.array(valid['y'])-np.array(forecast_valid)),2))) rms 57.494461930575149 #plot valid['Predictions'] = 0 valid['Predictions'] = forecast_valid.values plt.plot(train['y']) plt.plot(valid[['y', 'Predictions']]) Inference

Prophet (like most time series forecasting techniques) tries to capture the trend and seasonality from past data. This model usually performs well on time series datasets, but fails to live up to it’s reputation in this case.

As it turns out, stock prices do not have a particular trend or seasonality. It highly depends on what is currently going on in the market and thus the prices rise and fall. Hence forecasting techniques like ARIMA, SARIMA and Prophet would not show good results for this particular problem.

Long Short Term Memory (LSTM) Introduction

LSTMs are widely used for sequence prediction problems and have proven to be extremely effective. The reason they work so well is because LSTM is able to store past information that is important, and forget the information that is not. LSTM has three gates:

The input gate: The input gate adds information to the cell state

The forget gate: It removes the information that is no longer required by the model

The output gate: Output Gate at LSTM selects the information to be shown as output

For a more detailed understanding of LSTM and its architecture, you can go through the below article:

For now, let us implement LSTM as a black box and check it’s performance on our particular data.

Implementation #importing required libraries from sklearn.preprocessing import MinMaxScaler from keras.models import Sequential from keras.layers import Dense, Dropout, LSTM #creating dataframe data = df.sort_index(ascending=True, axis=0) new_data = pd.DataFrame(index=range(0,len(df)),columns=['Date', 'Close']) for i in range(0,len(data)): new_data['Date'][i] = data['Date'][i] new_data['Close'][i] = data['Close'][i] #setting index new_data.index = new_data.Date new_data.drop('Date', axis=1, inplace=True) #creating train and test sets dataset = new_data.values train = dataset[0:987,:] valid = dataset[987:,:] #converting dataset into x_train and y_train scaler = MinMaxScaler(feature_range=(0, 1)) scaled_data = scaler.fit_transform(dataset) x_train, y_train = [], [] for i in range(60,len(train)): x_train.append(scaled_data[i-60:i,0]) y_train.append(scaled_data[i,0]) x_train, y_train = np.array(x_train), np.array(y_train) x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1)) # create and fit the LSTM network model = Sequential() model.add(LSTM(units=50, return_sequences=True, input_shape=(x_train.shape[1],1))) model.add(LSTM(units=50)) model.add(Dense(1)), y_train, epochs=1, batch_size=1, verbose=2) #predicting 246 values, using past 60 from the train data inputs = new_data[len(new_data) - len(valid) - 60:].values inputs = inputs.reshape(-1,1) inputs = scaler.transform(inputs) X_test = [] for i in range(60,inputs.shape[0]): X_test.append(inputs[i-60:i,0]) X_test = np.array(X_test) X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1)) closing_price = model.predict(X_test) closing_price = scaler.inverse_transform(closing_price) Results rms=np.sqrt(np.mean(np.power((valid-closing_price),2))) rms 11.772259608962642 #for plotting train = new_data[:987] valid = new_data[987:] valid['Predictions'] = closing_price plt.plot(train['Close']) plt.plot(valid[['Close','Predictions']]) Inference

Wow! The LSTM model can be tuned for various parameters such as changing the number of LSTM layers, adding dropout value or increasing the number of epochs. But are the predictions from LSTM enough to identify whether the stock price will increase or decrease? Certainly not!

As I mentioned at the start of the article, stock price is affected by the news about the company and other factors like demonetization or merger/demerger of the companies. There are certain intangible factors as well which can often be impossible to predict beforehand.


Time series forecasting is a very intriguing field to work with, as I have realized during my time writing these articles. There is a perception in the community that it’s a complex field, and while there is a grain of truth in there, it’s not so difficult once you get the hang of the basic techniques.

Frequently Asked Questions

Q1. Is it possible to predict the stock market with Deep Learning?

A. Yes, it is possible to predict the stock market with Deep Learning algorithms such as moving average, linear regression, Auto ARIMA, LSTM, and more.

Q2. What can you use to predict stock prices in Deep Learning?

A. Moving average, linear regression, KNN (k-nearest neighbor), Auto ARIMA, and LSTM (Long Short Term Memory) are some of the most common Deep Learning algorithms used to predict stock prices.

Q3. What are the two methods to predict stock price?

A. Fundamental Analysis and Technical Analysis are the two ways of analyzing and predicting stock prices.


Toolset For Using Machine Learning Without Matlab

Although Matlab is a popular programming language in the field of machine learning, it is expensive. Nowadays, many programmers are looking for substitute toolkits to build machine learning algorithms. Thankfully, there are a number of open-source, economical solutions that can provide comparable features. This post will examine some of the top toolkits for employing machine learning outside of Matlab, including R packages like caret and randomForest as well as Python libraries like scikit-learn and TensorFlow.

List of toolset

There are many tools available for using machine learning without MATLAB. Here are some popular options −

1. Python

Python is a powerful and flexible programming language that has gained popularity for application in data analysis and machine learning. There are a number of machine-learning frameworks and tools that have been developed using this free and open-source language, which has a substantial and active development community.

Another is the well-known Python machine learning library PyTorch.Facebook created PyTorch, an open-source machine learning framework that offers a powerful tensor library for deep learning. Compared to rival frameworks, it is more adaptable and user-friendly due to its dynamic computational network.

Scikit-learn is another popular Python machine-learning package. It is a straightforward and effective data mining and data analysis tool that offers a variety of supervised and unsupervised learning methods for applications like classification, regression, and clustering.

Together with these libraries, Python also provides a wide range of additional beneficial machine-learning tools including Keras, Theano, and Pandas. Theano is a deep learning framework for numerical computing, Pandas is a data manipulation library that offers data structures for effective data analysis, and Keras is a high-level neural network library.

Generally, Python’s appeal in machine learning may be attributed to its simplicity, adaptability, and abundance of libraries and frameworks. Building and training machine learning models as well as analyzing and manipulating data for diverse applications are made simpler by these tools and frameworks.

2. R

R is a software environment and programming language for statistical computation and graphics. It also features several packages, like caret and randomForest, and is frequently used for machine learning applications.

R is a widely used programming language and computing environment for statistical computation and graphics. MATLAB has grown to be a well-liked option for data analysis, machine learning, and statistical modeling thanks to its large library of statistical and graphical tools.

R’s extensive library of packages created especially for data analysis and machine learning is one of the key factors contributing to its appeal in machine learning. Caret and randomForest are two of these tools that are frequently used for machine learning.

The R package Caret (Classification And Regression Training) offers a uniform interface for training and fine-tuning a wide range of machine-learning models. It supports a broad range of methods, including linear and nonlinear regression, decision trees, and support vector machines, and provides functions for data splitting, preprocessing, feature selection, and model training.

Another well-liked R package, RandomForest, offers the random forest technique implementation for classification and regression problems. Because of its capacity to manage high-dimensional data, cope with missing values, and handle relationships between variables, it is a preferred option for machine learning applications.

R features a wide range of other helpful machine learning packages, like the caretEnsemble package, which offers tools for merging several machine learning models, and the glmnet package, which offers effective generalized linear model implementations.

Overall, R’s large library of packages for statistical computation and data analysis makes it a popular language for machine learning.

3. RapidMiner

An integrated environment for model deployment, machine learning, and data preparation is provided by the data science platform RapidMiner. The interface is drag-and-drop and supports a wide range of data sources and formats.

Model deployment, machine learning, and data preparation can all be done in one integrated environment with the help of the potent data science platform RapidMiner. It seeks to simplify for users the processes of data collecting, machine learning model construction, and application in real-world scenarios.

The essential aspect is the rapid miner’s ability to use workflows to automate the machine learning process. The best-performing models may be generated quickly and easily, tested, and then put into production via a number of approaches.

Overall, RapidMiner is a capable and flexible data science platform that can be used for many machine learning and data analysis tasks. It is a well-liked option for both novice and experienced users because of its user-friendly drag-and-drop interface, wide selection of machine-learning algorithms, and compatibility with a number of data sources and formats.


An open-source platform for data analytics called KNIME offers a graphical user interface for creating data pipelines and processes. It may be expanded with plugins and customized nodes in addition to having several built-in nodes for data preparation, machine learning, and visualization.

An open-source platform for data analytics called KNIME offers a visual interface for creating data pipelines and processes. Even those without programming skills may use it easily, yet it nonetheless has cutting-edge features for applications involving machine learning and data analytics.

Moreover, KNIME enables distinctive nodes and plugins that are developed and shared by the user base. Now, users may enhance platform features to meet their own demands.

KNIME’s capacity to interact with various platforms and data sources like Hadoop, Spark, and R is another important aspect. As a result, working with big, complicated datasets and incorporating KNIME into current data ecosystems are made simple.

A variety of machine learning methods, such as decision trees, clustering, and regression models, are offered by KNIME. With the use of a straightforward drag-and-drop interface, these can be set up, trained, and then applied to fresh data inside the platform.

Last but not least, KNIME offers a wide selection of charts, graphs, and other visualizations as part of its rich support for data visualization. This enables users to study and comprehend their data in a number of ways and successfully share their conclusions with others.


Python is a powerful and flexible programming language that has gained popularity in data analysis and machine learning due to its simplicity, adaptability, and abundance of libraries and frameworks. RapidMiner provides an integrated environment for model deployment, machine learning, and data preparation, and KNIME offers a graphical user interface for creating data pipelines and processes. KNIME is a powerful and adaptable framework for data analytics that is suitable for both novice and expert users due to its large library of built-in nodes, support for new nodes, and plugins.

Interpreting Loss And Accuracy Of A Machine Learning Model

Machines are getting more intelligent than ever in the modern world. This is mostly brought on by machine learning’s rising significance. The process of teaching computers to learn from data and then utilize that information to make judgments or predictions is known as machine learning. Understanding how to judge the performance of these models is essential as more and more sectors start to rely on machine learning. In this blog article, we’ll examine the machine learning concepts of loss and accuracy and how they can be used to evaluate model efficacy.

What is Loss in Machine Learning?

In machine learning, loss refers to the error between expected and actual data. A machine learning model’s objective is to reduce this error or loss function. The loss function is a mathematical function that measures the discrepancy between output values that are expected and those that are actually produced. The performance of the model improves with decreasing loss. The gradients required to update the model’s parameters during training are calculated using the loss function, which is a crucial step in the training process. Depending on the issue being addressed, several loss functions are employed, such as cross-entropy loss for classification problems and mean squared error for regression problems. Since increasing prediction accuracy is the ultimate aim of every machine learning model, minimization of the loss function is essential. Developers and data scientists can build better models and boost their performance by grasping the idea of loss in machine learning.

What is Accuracy in Machine Learning?

In machine learning, accuracy is a crucial parameter for gauging how well the model predicts the future. It is calculated as the proportion of accurate forecasts to all of the model’s predictions. The performance of the model improves with increasing precision. When solving classification issues, accuracy is crucial since the model must accurately categorize examples into several groups. For instance, the proportion of emails that are accurately categorized as spam or not spam in a spam detection system serves as a gauge of the model’s accuracy. In many applications, maximizing accuracy is essential since poor forecasts might have serious repercussions.

Interpreting Loss and Accuracy Context of the Problem Being Solved

In machine learning, it’s essential to comprehend the context of the issue being handled in order to interpret a model’s performance. Different issues call for various accuracy and loss trade-offs. For instance, reducing false negatives is more crucial than reducing false positives in a medical diagnosis system. Maximizing accuracy is more significant in a fraud detection system than maximizing recall. Developers and data scientists can construct relevant metrics for evaluating the performance of the model by first understanding the context of the issue.

Trade-off Between Loss and Accuracy

In machine learning, loss, and accuracy are frequently trade-offs. A model that maximizes accuracy may not always be one that minimizes the loss function and vice versa. For instance, a model that overfits the training data in image recognition tasks may have a low loss yet perform badly on fresh data. In contrast, a model that underfits can have a bigger loss yet perform better with fresh data. The trade-off between accuracy and loss relies on the particular issue being resolved as well as the limitations of the application.

Importance of Considering the Validation Set

A validation set is a crucial consideration when evaluating a machine learning model’s performance. A portion of the dataset called the validation set is left aside so that the model can be tested on new data. When a model performs well on training data but poorly on new data, this helps prevent overfitting. Overfitting can be discovered by comparing the model’s performance on the validation set with the training set. Developers and data scientists can prevent overfitting by carefully weighing the model’s hyperparameters while monitoring the model’s accuracy and loss on the validation set.


To sum up, assessing a machine learning model’s loss and accuracy is a crucial stage in the machine learning process. The model’s performance can be evaluated, modifications can be made with knowledge, and the problem can be solved as intended can all be done by developers and data scientists. A machine learning model’s performance should be interpreted by taking into account the trade-offs between loss and accuracy, the context of the issue being solved, and the use of an appropriate validation set.

Role Master’S Degree In The Field Of Machine Learning


Today, machine learning is one of the most popular and prosperous computer industry subsectors. Due to its capability to manage enormous volumes of data and extract insights from them, machine learning has emerged as a crucial tool for businesses across a wide range of industries. Many are accessible in this field because there is such a high need for expertise. A Master’s degree in machine learning is among the most thorough ways to gain the abilities and information needed to pursue a job in this fascinating field.

In this article, we’ll discuss machine learning and master’s degrees.

Role Master’s Degree

Machine learning has become one of the IT industry’s most in-demand specialties in recent years, and for good reason. Machine learning has the potential to completely transform the way we live and work because to its capacity to process enormous volumes of data and spot patterns. The need for specialists with knowledge of machine learning has consequently grown considerably. A Master’s in Machine Learning is one way to gain the necessary abilities and information. The function of a Master’s degree in the subject of machine learning will be covered in this article.

A Master’s degree in machine learning, first and foremost, provides a solid understanding of the underlying concepts. This includes statistical methods, computer programs, and mathematical formulas. Students learn how to analyze huge data sets using machine learning techniques to acquire insights and make predictions. Also, students get hands-on training utilizing actual data sets and machine learning methods.

In addition to being essential for educating students for a range of career opportunities, a master’s degree in machine learning is also very important. Graduates can find employment as data scientists, machine learning engineers, artificial intelligence researchers, and more. You can develop your career in a number of industries, such as marketing, finance, healthcare, and technology, with a master’s degree in machine learning.

The lack of diversity is one of the main problems facing the machine learning profession. There is a considerable gender gap in the field, which is dominated by white and Asian men. But master’s degree programs in machine learning are addressing this problem. Several initiatives deliberately seek out underrepresented groups, including minorities and women, and support them. Also, some programs provide financial aid and scholarships to make the degree more available to a variety of individuals.

Furthermore, the cost of a Master’s degree in machine learning might be high, and the return on investment will rely on the student’s career objectives and the job market.

It is essential to carefully evaluate the benefits and drawbacks of acquiring a Master’s degree in machine learning. One should also consider any viable alternatives that might be less expensive or more suited to your needs and goals.

When seeking a Master’s degree in machine learning, the program’s staff and curriculum should be taken into consideration. Choose a program that offers a demanding and up-to-date curriculum that covers a wide range of topics and incorporates both fundamental concepts and cutting-edge methodologies. Selecting a school with knowledgeable professors engaged in active research and industry ties is also essential.

Finally, it’s crucial to keep in mind that machine learning is a field that is always evolving, with new techniques and tools being developed on a regular basis. With a master’s degree, one can begin a career in machine learning; nevertheless, it’s critical to stay current on new developments through ongoing education and professional development.


In conclusion, people seeking to pursue a career in this fascinating sector have a great opportunity with a Master’s degree in machine learning. Graduates of these programs are well-prepared for a wide range of employment prospects in several industries, having a solid foundation in both theory and practical applications. Even while completing a Master’s degree in machine learning can be pricey, the rewards are obvious. A Master’s degree in this area can offer the skills and information required to survive in the quickly developing field of data science, from beneficial networking possibilities to a thorough comprehension of the fundamental concepts of machine learning.

Data Scientist Vs Machine Learning

Differences Between Data Scientist vs Machine Learning

Hadoop, Data Science, Statistics & others

Data Scientist

Standard tasks:

Allocate, aggregate, and synthesize data from various structured and unstructured sources.

Explore, develop, and apply intelligent learning to real-world data and provide essential findings and successful actions based on them.

Analyze and provide data collected in the organization.

Design and build new processes for modeling, data mining, and implementation.

Develop prototypes, algorithms, predictive models, and prototypes.

Carry out requests for data analysis and communicate their findings and decisions.

In addition, there are more specific tasks depending on the domain in which the employer works, or the project is being implemented.

Machine Learning

The Machine Learning Engineer position is more “technical.” ML Engineer has more in common with classical Software Engineering than Data Scientists. It helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

The standard tasks of ML Engineers are generally like Data scientists. You also need to be able to work with data, experiment with various Machine Learning algorithms that will solve the task, and create prototypes and ready-made solutions.

Strong programming skills in one or more popular languages (usually Python and Java) and databases.

Less emphasis on the ability to work in data analysis environments but more emphasis on Machine Learning algorithms.

R and Python for modeling are preferable to Matlab, SPSS, and SAS.

Ability to use ready-made libraries for various stacks in the application, for example, Mahout, Lucene for Java, and NumPy / SciPy for Python.

Ability to create distributed applications using Hadoop and other solutions.

As you can see, the position of ML Engineer (or narrower) requires more knowledge in Software Engineering and, accordingly, is well suited for experienced developers. The case often works when the usual developer must solve the ML task for his duty, and he starts to understand the necessary algorithms and libraries.

Head-to-Head Comparison Between Data Scientist and Machine Learning (Infographics)

Below are the top 5 differences between Data scientists and Machine Learning:

Key Difference Between Data Scientist and Machine Learning

Below are the lists of points that describe the key Differences Between Data Scientist and Machine Learning:

Machine learning and statistics are part of data science. The word learning in machine learning means that the algorithms depend on data used as a training set to fine-tune some model or algorithm parameters. This encompasses many techniques, such as regression, naive Bayes, or supervised clustering. But not all styles fit in this category. For instance, unsupervised clustering – a statistical and data science technique – aims at detecting clusters and cluster structures without any a-prior knowledge or training set to help the classification algorithm. A human being is needed to label the clusters found. Some techniques are hybrid, such as semi-supervised classification. Some pattern detection or density estimation techniques fit into this category.

Data science is much more than machine learning, though. Data in data science may or may not come from a machine or mechanical process (survey data could be manually collected, and clinical trials involve a specific type of small data), and it might have nothing to do with learning, as I have just discussed. But the main difference is that data science covers the whole spectrum of data processing, not just the algorithmic or statistical aspects. Data science also covers data integration, distributed architecture, automated machine learning, data visualization, dashboards, and Big data engineering.

Data Scientist and Machine Learning Comparison Table

Feature Data Scientist Machine Learning

Data It mainly focuses on extracting details of data in tabular or images. It mainly focuses on algorithms, polynomial structures, and word adding.

Complexity It handles unstructured data, and it works with a scheduler. It uses Algorithms and mathematical concepts, statistics, and spatial analysis.

Hardware Requirement Systems are Horizontally scalable and have High Disk and RAM storage. It requires Graphic processors and Tensor Processors, that is very high-level hardware.

Skills Data Profiling, ETL, NoSQL, Reporting. Python, R, Maths, Stats, SQL Model.

Focus Focuses on abilities to handle the data. Algorithms are used to gain knowledge from huge amounts of data.


Machine learning helps you learn the objective function, which plots the inputs to the target variable and independent variables to the dependent variables.

A Data scientist does a lot of data exploration and arrives at a broad strategy for tackling it. He is responsible for asking questions about the data and finding what answers one can reasonably draw from the data. Feature engineering belongs to the realm of Data scientists. Creativity also plays a role here, and An Machine Learning engineer knows more tools and can build models given a set of features and data – as per directions from the Data Scientist. The realm of Data preprocessing and feature extraction belongs to ML engineers.

Data science and examination utilize machine learning for this archetypal validation and creation. It is vital to note that all the algorithms in this model creation may not come from machine learning. They can arrive from numerous other fields. The model desires to be kept relevant always. If the situations change, the model we created earlier may become immaterial. The model must be checked for certainty at different times and adapted if its confidence reduces.

Data science is a whole extensive domain. If we try to put it in a pipeline, it would have data acquisition, data storage, data preprocessing or cleaning, learning patterns in data (via machine learning), and using knowledge for predictions. This is one way to understand how machine learning fits into data science.

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