https://github.com/linsanity03/titanic_prediction
A visualization on probability of people surviving titanic
https://github.com/linsanity03/titanic_prediction
classifier-model machine-learning prediction-model tensorflow
Last synced: about 2 months ago
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A visualization on probability of people surviving titanic
- Host: GitHub
- URL: https://github.com/linsanity03/titanic_prediction
- Owner: LINSANITY03
- Created: 2023-09-16T11:09:11.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-09-18T10:23:07.000Z (over 2 years ago)
- Last Synced: 2025-04-05T06:41:42.062Z (about 1 year ago)
- Topics: classifier-model, machine-learning, prediction-model, tensorflow
- Language: Python
- Homepage:
- Size: 28.3 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Prediction model of surviving possibilities
In this project we use pandas to collect data from a source and in-built tensorflow model to train the data.
To run this project,
- Create a virutal environment and activate the environment.
` virtualenv venv
\venv\Scripts\activate`
- Install the required dependencies.
`pip install -r requirements.txt
`
- Run the **prediction_main.py** file.
`python prediction_main.py`
**1. Data Collection:**
We get titanic training and evaluation data from google drive links.
```
import pandas as pd
...
dftrain = pd.read_csv(
'https://storage.googleapis.com/tf-datasets/titanic/train.csv') # training data
dfeval = pd.read_csv(
'https://storage.googleapis.com/tf-datasets/titanic/eval.csv') # testing data
```
**2. Feature Extraction:**
Using the in-built feature column function of tensorflow, we get all the unique value from each column of the pandas file.
```
CATEGORICAL_COLUMNS = ['sex', 'n_siblings_spouses', 'parch', 'class', 'deck',
'embark_town', 'alone']
NUMERIC_COLUMNS = ['age', 'fare']
feature_columns = []
# we use the inbuilt function in tensorflow to get all the unique value represented in the data of certain features
for feature_name in CATEGORICAL_COLUMNS: # gets a list of all unique values from given feature column
vocabulary = dftrain[feature_name].unique()
feature_columns.append(tf.feature_column.categorical_column_with_vocabulary_list(
feature_name, vocabulary))
# similar for the numeric ones we get the features in float format
for feature_name in NUMERIC_COLUMNS:
feature_columns.append(tf.feature_column.numeric_column(
feature_name, dtype=tf.float32))
```
**3. Data Preparation:**
We need to make sure the data are in appropritate format for the tensorflow model. So, we convert the datas into data.Dataset object using tf.data.Dataset function
```
# create tf.data.Dataset object with data and its label
ds = tf.data.Dataset.from_tensor_slices((dict(data_df), label_df))
```
**4. Choosing a Model:**
Our goal is to predict the chance of survivility. So, a simple linear model would do the trick.
```
linear_est = tf.estimator.LinearClassifier(feature_columns=feature_columns)
```
**5. Training the model:**
We use the data we convert to data.Dataset object to the model.
```
linear_est.train(train_input_fn) # train
```
**6. Evaluate the model:**
Test the unseen dataset to measure the performance of the trained model.
```
result = linear_est.evaluate(eval_input_fn)
```
**7. Make prediction:**
Using the evaluated model predict the survivor possibilty and plot the stats into graph using matplot for better readability.
```
pred_dicts = list(linear_est.predict(eval_input_fn))
probs = pd.Series([pred['probabilities'][1] for pred in pred_dicts])
probs.plot(kind='hist', bins=20, title='predicted probabilities')
plt.show()
```
