Thirumala Reddy
December 16, 2022
STEPS IN DATA PREPROCESSING:-Â
Â Â
Â In most cases removing the null values is not preferred. If we have millions of records & in that only less number of records are missing we can remove the null values.
The best way of dealing with null values is by filling in the null values. Depending on the type of data we have we can fill the null values by using mean, median, mode, ffill & bfill.
--> Let's see some of the use cases to fill null values:-
-->Â Â After filling in the null values with any of the methods, plot a graph between the old values i.e. before filling in the null values & the new values i.e. after filling in the null values. This will help us to find the change in the distribution of data before & after filling in the null values.
Advantages And Disadvantages of Mean/Median Imputation:-
Advantages:-
Disadvantages:-
By using the machine learning models such as KNN & Iterative Imputer methods also we can fill the null value
Click here to see how to handle null values
3. Handling categorical columns
Let's discuss types of Encoding. There are two types of encoding
1) Nominal Encoding
2) Ordinal Encoding
1)Nominal Encoding:-
Nominal encoding is nothing but the features where variables have no order or rank to this variable's feature.
-->The different types of Nominal Encoding are
Among these, all Nominal Encoding One Hot Encoding is preferred.
Â If categories > 2 & <7 use One Hot Encoder. By using a Label Encoder for the dataset which more than 2 categories there is a chance of High Bias so by using One hot encoder we can remove the bias of higher numbers.
Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â
2. Ordinal Encoder
Ordinal Encoding is nothing but theÂ feature where variables have someÂ order or rank.
Label Encoding
Label Encoder assigns a unique number (starting from 0) to each category.
-->If we have nan values present in the data & after doing Label Encoding, the nan value also will be classified into separate categories.
Â Â Â Â Â Â Â
Advantages: -
Â Â Â Â 1)Straightforward to implement
Â Â Â Â 2)Does not require hours of variableÂ exploration
Â Â Â Â 3)Does not expand massively the feature space(no of columns in the dataset)
Disadvantages: -
1)Does not add any information that may make the variablesÂ more predictive
2)Does not keep the information of the ignored labels
Click here to see how to apply one hot encoder & Label Encoder
-->We can handle categorical columns by using a library present in pandas i.e.Â get_dummies
Click here to know how to apply get_dummies to handle categorical features
Â 4. Handle Outlier
Â Â Â Â a. Remove outlier (Not recommended)
Â Â Â Â b. Clip
Â Â Â Â c. Make outliers as Nulls, and do Fill in Missing
Which Machine LearningÂ Models Are Sensitive To Outliers?
How to find out the outliers:-
1)By Using Box plot:-
We can find the outliers by using a box plot. We can consider the values as outliers if they are less than the minimum value & greater than the maximum value from the Box plot
Click here to see how to handle Outliers
5. Feature SelectionÂ
Â Â Â Â Â a. Manual AnalysisÂ
Â Â Â Â b. UnivariateÂ Selection
Â Â Â Â c. Feature Importance
Â Â Â Â d. Correlation Matrix with Heatmap
Â Â Â Â e. PCA (Principle component analysis)
-->For selecting the features manually we will take the help of domain exports.
Â Â Â Ex: - While solving Banking domain problem statements we will take the help of banking domain people for selecting the features.
Univariate selection:-
Â Â In the univariate selection, we use the SelectKBest library present inside learn. SelectKBest internally applies the chi-square test and gives the out chi-square score. Based on this we will select the top features.
Click here to see how to select features by using the univariate selection
Correlation Matrix with Heatmap
In this, we construct the correlation matrix with a heatmap, and from the heat map, we can get what are the features that are more important for predicting the output.
-->From the above heatmap, we can observe that the price_range is the output columns & with respect to output columns ram has the highest correlation value of 0.92 next is battery power, etc.
Click here to see how to select features by using a correlation matrix with a heatmap
6. Scale your dataÂ (normalize data in a certain range)
Â MinMax Scaler, Standard Scaler, Robust Scaler
Scaling helps to bring all Columns into a Particular Range
1)MinMax Scaler: -
MinMax scaler converts the data between 0 & 1 by using the min-max formula.
-->Below is the formula of the min-max scaler.
Robust Scaler is used to scale the feature to median and quantiles Scaling using median and quantiles consists of subtracting the median from all the observations and then dividing by the interquartile difference.
IQR = 75th quantile - 25th quantile
X_scaled = (X - X.median) / IQR
Click here to see how to apply scaling
Some machine learning algorithms like linear and logistic assume that the features are normally distributed
If the data is not normally distributed follow the below steps
- logarithmic transformation
- reciprocal transformation
- square root transformation
- exponential transformation (more general, you can use any exponent)
- box cox transformation
Refer to this for all the above transformation technique implementation
Â WhichÂ Â Models require Scaling of the data?
1)Linear Regression-->Require
2)Logistic Regression-->Require
3)Decision Tree-->Not Require
4)Random Forest-->Not Require
5)XG Boost-->Not Require
6)KNN-->Require
7)K-Means-->Require
8)ANN-->Require
i.e. distance-based models & the models which use the concept of Gradient Descent require Scaling.
-->fit_transform is applied only on theÂ trainingÂ dataset & on the testing dataset only transform is used, this is done to avoid data leakage.
Click here for complete end-to-endÂ processing
Let's Discuss some of the Automated EDA Library
There are different kinds of Automated EDA Library.
-->Some of them are
Â Â Â Â Â Â 1)DTale
Â Â Â Â Â Â 2)Pandas Profiling
Â Â Â Â Â Â 3)Seeetviz
Â Â Â Â Â Â 4)autoviz
Â Â Â Â Â Â 5)DataPrep
Â Â Â Â Â Â 6)Pandas Visual Analysis
Click here to see how to apply the Automated EDA Library
Dataisgood is on a mission to ensure that everyone has the opportunity to thrive in an inclusive environment that fosters equal opportunities for advancement and progress. At Dataisgood, we empower individuals with live, hands-on training led by industry experts. Our goal is to facilitate successful transitions for those from non-tech backgrounds, equipping them with the skills and knowledge needed to excel in the tech industry. Additionally, we offer upskilling and reskilling opportunities through our industry-approved training programs, ensuring that professionals stay ahead inÂ theirÂ careers
Dataisgood LLC.
447 Broadway,
NY 10013, USA
Ph:Â +1 718-682-7717
Addictive Learning Technology Pvt Ltd
B-75, Sector 63 Noida, 201301
Uttar Pradesh, India
Ph:+91-8700627800
Addictive Learning Technology Pvt Ltd
Corporate Office: 576, Block C,Sushant Lok Phase I, Sector 43, Gurugram, Haryana 122002
Ph:+91-8700627800
Skill Arbitrage Technology, Inc.
8 The Green,
Dover,Â DEÂ 19901
Ph:+91-8700627800