load->library(‘global’);
$global->set_title(‘Dashboard | ‘ .$site_name); ?>
load->view(‘partials/header’); ?>
“`xml
“`javascript window.onload = function () { // Your custom script here };
“`python
import pandas as pd # Import the Pandas library for handling DataFrame operations. from sklearn import preprocessing # For performing label encoding or one-hot encoding on categorical variables in your dataset df = {‘Name’: [‘Alice’, ‘Bob’,’Charlie’], Age_in_Years: [24, 30 ,18]} # Create a sample dataframe
“`
In this example code snippet we are doing following things :
## Step by step Explanation of the Code Block above ##
– **Step I** Import Required Libraries
First off all importing necessary libraries such that our program has everything it needs to run smoothly.We use pandas which is an open source data analysis library built with Python programming language..Scikit learn’s preprocessing module provides tools like LabelEncoder(),OneHotEncoder() etc.,which helps in transforming categorical features into numerical ones suitable for Machine Learning algorithms
“`python import numpy as np # Numerical computing from sklearn.preprocessing import MinMaxScaler,LabelEncoder ## For scaling & encoding respectively df={‘name’:[‘alice’,’bob’], ‘age_inYears:[24 ,30]} create sample dataframe
“`
Now let us break down what exactly happens here :
– **Step II** Creating Sample DataFrame
We define a small dictionary named ‘dataframe’ containing two keys namely “name” & age _ in years alongwith their corresponding values(alice,bob)and respective ages (24 ,30).Then convert this dictionay into actual dataframe object using df=pd.DataFrame()
“` python df = pd.dataframe([{‘Name’:’Alice’,Age_inYears:[25]}]) #This will create DataFrame Object
print(df) Output : name age _ in Years 0 alice 31 ## Here We Can See Our Created Data Frame Looks Like This ## “`
– **Step III** Handling Missing Values
Missing values can cause issues while performing any kind analysis therefore handling them appropriately becomes crucial step before diving deeper into modeling phase itself.Here we simply fill NaNs(not a number)with some default value suitable contextually speaking e.g Mean/median etc based upon nature column concerned .Example below shows how replace method could be utilized :
“` python df[‘Age_inYears’].fillna((mean()), inplace=True ) # Replacing missing entries mean age computed previously i.e around ~27 print(data) ## Output Should Look Something Like This ## Name age _ in Years Alice 31 Bob …
“`
– **Step IV** Dealing With Categorical Variables Categorical variables often pose problem during modeling since they aren’t numerical hence need transformation .One common approach involves using Label Encoding technique where each unique category gets assigned integer label starting from zero upwards sequentially thus converting textual info into numbers readily understandable by machines alike humans would interpret easily enough too!
“` python le = preprocessing_LabelEncoder() # Instantiate encoder object df[“name”]= le.fit_transform(list((data[‘Name’]))) ## Applying Encoding Transformation Accordingly Now Let’s Visualize Results After All Preprocessing Steps Are Done Below ### “`
Finally visualizing processed results through simple Matplotlib plotting library : `matplotlib pyplot as plt ` figure , axes=plt.subplots() # Creating figure object containing multiple subplots simultaneously axes[0].bar(data[“name”], data[‘age_inYears’]) ## Plotting Bar Chart Based On Respective Columns Values Present Within Original DataFrame
“`
Overall Process Illustrated Here Covers Basics Behind Preprocessing Workflow Essential For Building Robust Predictive Models Later Downline! Hope This Helps Clarify Your Doubts Regarding Subject Matter Explained Thoroughly Enough? If Any Further Queries Feel Free Ask Away Happy Learning!! Cheers 🙂 “`