{"id":132691,"date":"2023-03-25T10:54:31","date_gmt":"2023-03-25T10:54:31","guid":{"rendered":"https:\/\/blog.finxter.com\/?p=1242421"},"modified":"2023-03-25T10:54:31","modified_gmt":"2023-03-25T10:54:31","slug":"how-i-created-a-machine-learning-web-application-using-django","status":"publish","type":"post","link":"https:\/\/sickgaming.net\/blog\/2023\/03\/25\/how-i-created-a-machine-learning-web-application-using-django\/","title":{"rendered":"How I Created a Machine Learning Web Application Using Django"},"content":{"rendered":"\n
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5\/5 – (1 vote) <\/div>\n<\/p><\/div>\n

Deploying a Machine Learning model<\/a> as a web application makes it easy for others with little or no programming experience to use it. <\/p>\n

In previous tutorials, where I explained how I created a house price prediction app<\/a> and a loan eligibility app<\/a>, we made use of Streamlit. Streamlit is easy to use. This is why it is a popular choice for data scientists.<\/p>\n

In a world where learning one framework isn\u2019t enough, won\u2019t it be nice to learn how to accomplish this using Django? <\/p>\n

Understandably, Django can be tough for beginners. The only remedy though is constant practice. If you have been going through some of my tutorials on Django<\/a>, there is no doubt that you have become familiar with the process.<\/p>\n

Therefore, in this tutorial, we will add to your knowledge by creating a machine learning application using Django. Guess what we will used for prediction? The famous Titanic dataset!<\/p>\n

Developing Machine Learning Model<\/h2>\n

The Machine Learning classification problem aims to predict the survival of the passengers based on their attributes. There are multiple steps involved in creating a Machine Learning model. To keep things simple, we will skip those steps and focus on showing how to implement Machine Learning in Django.<\/p>\n

Create a folder for this project. Then, inside the folder, create a file named model.py<\/code>. This is where we will develop our model. You can download the dataset on my GitHub page<\/a>.<\/p>\n

import pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.linear_model import LogisticRegression\nimport pickle df = pd.read_csv('titanic.csv', index_col=0) # selecting the features we need\ndf = df[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked', 'Survived']] # encoding the column to a numberic value\ndf['Sex'] = df['Sex'].map({'male': 0, 'female': 1}) # converting the Age column to numberic type\ndf['Age'] = pd.to_numeric(df.Age) # filling the null values\ndf['Age'] = df.Age.fillna(np.mean(df.Age)) # creating additional features from Embarked columns after converting to dummy variables\ndummies = pd.get_dummies(df.Embarked)\ndf = pd.concat([df, dummies], axis=1)\ndf.drop(['Embarked'], axis=1, inplace=True) X = df.drop(['Survived'], axis=1)\ny = df['Survived'] # scaling the features\nscaler = MinMaxScaler(feature_range=(0,1))\nX_scaled = scaler.fit_transform(X) model = LogisticRegression(C=1)\nmodel.fit(X_scaled, y) # saving model as a pickle\npickle.dump(model, open('titanic.pkl', 'wb'))\npickle.dump(scaler, open('scaler.pkl', 'wb'))\n<\/pre>\n
After importing the CSV<\/a> file, we saw that there are missing values in the features selected. We simply fill them up with the mean of the values. We convert the Embarked column to a dummy variable. Then, we add them to the features.<\/p>\n
We are using LogisticRegression<\/a><\/code> as the Machine Learning algorithm to make this prediction. Finally, we save the model as a pickle object<\/a> to be used later.<\/p>\n
Creating Django Project<\/h2>\n
As covered in previous tutorials, the steps to set up a new Django project are as follows:<\/p>\n