# Introduction to Preprocessing for Machine Learning

#### Your data needs to be suited to your model !!

Before performing any preprocessing in data, you first need to know about your data. For this, we have some useful attributes like **.columns, .dtype, .describe().**

.column: generate a list of features.

.dtype: tells about the datatype of each feature.

.describe(): tells about the basic statistics of your data like mean, std, min, max.

Dataset may contain missing values, incompatible datatypes etc. that needs to be taken care.

### Removing missing values

Let there be a dataframe ‘df’ for a dataset.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583762818/0205b632-4038-4fb9-8f13-d8a2435ec386.png)

To drop all row that contain missing values use **dropna()**. Eg: **df.dropna().**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583764569/fbe538c7-5176-4aab-aa70-3c40e5ec2d3e.png)

To drop specific rows, pass index labels to drop function. Eg: **df.drop(\[1,2,3\]).**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583766001/52e5116e-97e0-47db-8cb2-15838522d3d5.png)

To drop a column (let column name be A) with missing values, use **df.drop(“A”,axis=1))**.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583767749/c65b8097-4c2d-4274-889d-7020fef2483f.png)

To count null values in column use **df\[“B”\].isnull().sum().**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583769875/6055a4d7-1222-49f8-a3dd-2f9f1a86c3cb.png)

To count non-null values use, **df\[df\[“B”\].notnull()\])**

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1745583770849/7a0550ac-29d9-4821-bc04-87227ca6a816.png)

### Working with datatypes

The basics data types present in a dataset are:

**object:** string/mixed type

**int64:** integer

**float64:** floating values

Sometimes, there is a need to change datatypes of certain features. To do so, use, **df\[“col\_name”\] = df\[“col\_name”\].astype(“data\_type\_name”)**

Eg: If you wish to convert a column named “A” datatype from integer to float.

**df\[“A”\] = df\[“A”\].astype(“float64”)**

### Training and Tests sets

One of the necessary steps of preprocessing should be splitting of the dataset, to avoid overfitting. If a model is trained on the entire training set, there will be no data left to test that model and the model will know all the data by heart. Having a test set enables a model to test itself on unseen data. Splitting can be done as follows:

> *from sklearn.model\_selection import train\_test\_split*

> *train\_X, test\_X, train\_y, test\_y = train\_test\_split(X, y)*

By default, data is splitted in **75%** training set and **25%** test set. To customize the splitting according to the need, use, **test\_size** parameter, for eg: test\_size = 0.3 will split the dataset in 70% training set and 30% test set.

Somtimes the splitting can be biased and this could bias the model, for eg: training set may contain same labels throughout the set and it ask model to test for the label that the model has never seen. This means there is a presence of imbalance classes. So, a good technique to handle these imbalanced classes is **stratified sampling.** It takes into account the distribution of classes in the dataset.

For example, if we have a dataset with 1000 samples, 800 of class A and 200 of class B. We want 80% of class A and 20% of class B for each set. Below is the distribution that we want,

Training set: 750 samples, 600 of class A and 150 of class B.

Testing set: 250 samples, 200 of class A and 50 of class B.

> y\[“labels”\].value\_counts()

The above piece of code will output:

> Class A 800

> Class B 200

To achieve uniform distribution, set **stratify = y** in train\_test\_split

> train\_X, test\_X, train\_y, test\_y = train\_test\_split(X, y, stratify=y)

> train\_y\[“labels”\].value\_counts()

The above piece of code will output:

> Class A 600

> Class B 150

> test\_y\[“labels”\].value\_counts()

The above piece of code will output:

> Class A 200

> Class B 50

*It can be seen that the distribution of classes is in accordance with the original* ***y*** *class distribution*