From 54dd309c88d932c6c76009001b4018df6ead338b Mon Sep 17 00:00:00 2001
From: Ayush Shivani <ashivani@aicrowd.com>
Date: Thu, 24 Sep 2020 14:53:21 +0530
Subject: [PATCH] baseline udpate
---
README.md | 2 +
.../CRDIO_baseline-checkpoint.ipynb | 453 ++---
baselines/CRDIO_baseline.ipynb | 453 ++---
baselines/train.csv | 1701 -----------------
4 files changed, 244 insertions(+), 2365 deletions(-)
delete mode 100644 baselines/train.csv
diff --git a/README.md b/README.md
index 653ad97..ddd13b4 100644
--- a/README.md
+++ b/README.md
@@ -18,7 +18,9 @@ To know about given attributes click [here](https://gitlab.aicrowd.com/aicrowd/p
# 📠Files
Following files are available in the `resources` section:
+
- `train.csv` - (`1700` samples) File that should be used for training and validation purposes by the user.
+
- `test.csv` - (`426` samples) File that will be used for actual evaluation for the leaderboard score.
# 🚀 Submission
diff --git a/baselines/.ipynb_checkpoints/CRDIO_baseline-checkpoint.ipynb b/baselines/.ipynb_checkpoints/CRDIO_baseline-checkpoint.ipynb
index 8e80c21..3d0ccc2 100644
--- a/baselines/.ipynb_checkpoints/CRDIO_baseline-checkpoint.ipynb
+++ b/baselines/.ipynb_checkpoints/CRDIO_baseline-checkpoint.ipynb
@@ -4,79 +4,56 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Baseline for crdio Educational Challenge on AIcrowd\n",
- "#### Author : Shubham Sharma"
+ ""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## To open this notebook on Google Computing platform Colab, click below!"
+ "# Baseline for [CRDIO](https://www.aicrowd.com/challenges/crdio) Challenge on AIcrowd\n",
+ "#### Author : Shubham Sharma"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)\n"
+ "## Download Necessary Packages 📚"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
"source": [
- "## Download Necessary Packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: numpy in /home/ayush/.local/lib/python3.7/site-packages (1.18.1)\n",
- "Requirement already satisfied: pandas in /home/ayush/.local/lib/python3.7/site-packages (0.25.0)\n",
- "Requirement already satisfied: numpy>=1.13.3 in /home/ayush/.local/lib/python3.7/site-packages (from pandas) (1.18.1)\n",
- "Requirement already satisfied: pytz>=2017.2 in /home/ayush/.local/lib/python3.7/site-packages (from pandas) (2019.3)\n",
- "Requirement already satisfied: python-dateutil>=2.6.1 in /home/ayush/anaconda3/lib/python3.7/site-packages (from pandas) (2.8.0)\n",
- "Requirement already satisfied: six>=1.5 in /home/ayush/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
- "Requirement already satisfied: scikit-learn in /home/ayush/.local/lib/python3.7/site-packages (0.21.3)\n",
- "Requirement already satisfied: scipy>=0.17.0 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (1.4.1)\n",
- "Requirement already satisfied: joblib>=0.11 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (0.14.0)\n",
- "Requirement already satisfied: numpy>=1.11.0 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (1.18.1)\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "!{sys.executable} -m pip install numpy\n",
- "!{sys.executable} -m pip install pandas\n",
- "!{sys.executable} -m pip install scikit-learn"
+ "!pip install numpy\n",
+ "!pip install pandas\n",
+ "!pip install scikit-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Download data\n",
- "The first step is to download out train test data. We will be training a classifier on the train data and make predictions on test data. We submit our predictions\n"
+ "## Download Data\n",
+ "The first step is to download out train test data. We will be training a model on the train data and make predictions on test data. We submit our predictions\n"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "#Donwload the datasets\n",
- "!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/public/crdio/v0.1/test.csv\n",
- "!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/public/crdio/v0.1/test.csv"
+ "!rm -rf data\n",
+ "!mkdir data \n",
+ "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/crdio/v0.1/test.csv\n",
+ "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/crdio/v0.1/train.csv\n",
+ "!mv test.csv data/test.csv\n",
+ "!mv train.csv data/train.csv\n",
+ "\n"
]
},
{
@@ -89,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -107,277 +84,92 @@
"metadata": {},
"source": [
"## Load Data\n",
- "We use pandas library to load our data. Pandas loads them into dataframes which helps us analyze our data easily. Learn more about it [here](https://www.tutorialspoint.com/python_data_science/python_pandas.htm)"
+ "- We use pandas 🼠library to load our data. \n",
+ "- Pandas loads the data into dataframes and facilitates us to analyse the data. \n",
+ "- Learn more about it [here](https://www.tutorialspoint.com/python_data_science/python_pandas.htm) 🤓"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "train_data_path = \"train.csv\" #path where data is stored"
+ "all_data_path = \"data/train.csv\" #path where data is stored"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "train_data = pd.read_csv(train_data_path) #load data in dataframe using pandas"
+ "all_data = pd.read_csv(all_data_path) #load data in dataframe using pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Visualize the data"
+ "## Visualize the data 👀"
]
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
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- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>LBE</th>\n",
- " <th>LB</th>\n",
- " <th>AC</th>\n",
- " <th>FM</th>\n",
- " <th>UC</th>\n",
- " <th>ASTV</th>\n",
- " <th>MSTV</th>\n",
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- " <tr>\n",
- " <th>3</th>\n",
- " <td>120.0</td>\n",
- " <td>120.0</td>\n",
- " <td>3.0</td>\n",
- " <td>1.0</td>\n",
- " <td>2.0</td>\n",
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- " <td>0.0</td>\n",
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- ],
- "text/plain": [
- " LBE LB AC FM UC ASTV MSTV ALTV MLTV DL ... B C \\\n",
- "0 135.0 135.0 8.0 2.0 7.0 60.0 1.0 0.0 5.2 0.0 ... 1.0 0.0 \n",
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- "4 132.0 132.0 6.0 0.0 6.0 27.0 1.7 0.0 11.3 0.0 ... 1.0 0.0 \n",
- "\n",
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- "[5 rows x 34 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_data.head()"
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can see the dataset contains 21 columns,where columns 1-20 denotes the information about the person that is called and the last column tell whether he subcribed the service or not."
+ "The dataset consists of `24` attributes out of which first `23` attributes describes details of `CTGs` features and last attribute called `NSP` is used to classify these `CTGs` on the basis of fetal state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Split Data into Train and Validation\n",
- "Now we want to see how well our classifier is performing, but we dont have the test data labels with us to check. What do we do ? So we split our dataset into train and validation. The idea is that we test our classifier on validation set in order to get an idea of how well our classifier works. This way we can also ensure that we dont [overfit](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) on the train dataset. There are many ways to do validation like [k-fold](https://machinelearningmastery.com/k-fold-cross-validation/),[leave one out](https://en.wikipedia.org/wiki/Cross-validation_(statistics), etc"
+ "## Split Data into Train and Validation 🔪\n",
+ "- The next step is to think of a way to test how well our model is performing. we cannot use the test data given as it does not contain the data labels for us to verify. \n",
+ "- The workaround this is to split the given training data into training and validation. Typically validation sets give us an idea of how our model will perform on unforeseen data. it is like holding back a chunk of data while training our model and then using it to for the purpose of testing. it is a standard way to fine-tune hyperparameters in a model. \n",
+ "- There are multiple ways to split a dataset into validation and training sets. following are two popular ways to go about it, [k-fold](https://machinelearningmastery.com/k-fold-cross-validation/), [leave one out](https://en.wikipedia.org/wiki/Cross-validation_statistics). ðŸ§\n",
+ "- Validation sets are also used to avoid your model from [overfitting](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) on the train dataset."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
+ "X_train, X_val= train_test_split(all_data, test_size=0.2, random_state=42) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Here we have selected the size of the testing data to be 20% of the total data. You can change it and see what effect it has on the accuracies. To learn more about the train_test_split function [click here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
+ "- We have decided to split the data with 20 % as validation and 80 % as training. \n",
+ "- To learn more about the train_test_split function [click here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). 🧠\n",
+ "- This is of course the simplest way to validate your model by simply taking a random chunk of the train set and setting it aside solely for the purpose of testing our train model on unseen data. as mentioned in the previous block, you can experiment 🔬 with and choose more sophisticated techniques and make your model better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now, since we have our data splitted into train and validation sets, we need to get the label separated from the data."
+ "- Now, since we have our data splitted into train and validation sets, we need to get the corresponding labels separated from the data. \n",
+ "- with this step we are all set move to the next step with a prepared dataset."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -389,35 +181,52 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Define the Classifier\n",
- "Now we come to the juicy part. We have fixed our data and now we train a classifier. The classifier will learn the function by looking at the inputs and corresponding outputs. There are a ton of classifiers to choose from some being [Logistic Regression](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc), [SVM](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Random Forests](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Decision Trees](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052), etc. \n",
- "Tip: A good model doesnt depend solely on the classifier but on the features(columns) you choose. So make sure to play with your data and keep only whats important. "
+ "# TRAINING PHASE ðŸ‹ï¸"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define the Model\n",
+ "\n",
+ "- We have fixed our data and now we are ready to train our model. \n",
+ "\n",
+ "- There are a ton of classifiers to choose from some being [Logistic Regression](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc), [SVM](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Random Forests](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Decision Trees](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052), etc.🧠\n",
+ "\n",
+ "- Remember that there are no hard-laid rules here. you can mix and match classifiers, it is advisable to read up on the numerous techniques and choose the best fit for your solution , experimentation is the key. \n",
+ " \n",
+ "- A good model does not depend solely on the classifier but also on the features you choose. So make sure to analyse and understand your data well and move forward with a clear view of the problem at hand. you can gain important insight from [here](https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2).🧠"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# classifier = SVC(gamma='auto')\n",
+ "classifier = SVC(gamma='auto')\n",
"\n",
- "#from sklearn.linear_model import LogisticRegression\n",
- "classifier = LogisticRegression()"
+ "# from sklearn.linear_model import LogisticRegression\n",
+ "# classifier = LogisticRegression()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We have used [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html#classification) as a classifier here and set few of the parameteres. But one can set more parameters and increase the performance. To see the list of parameters visit [here](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)."
+ "- To start you off, We have used a basic [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html#classification) classifier here. \n",
+ "- But you can tune parameters and increase the performance. To see the list of parameters visit [here](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html). \n",
+ "- Do keep in mind there exist sophisticated techniques for everything, the key as quoted earlier is to search them and experiment to fit your implementation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can also use other classifiers. To read more about sklean classifiers visit [here](https://scikit-learn.org/stable/supervised_learning.html). Try and use other classifiers to see how the performance of your model changes. Try using [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) or [MLP](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) and compare how the performance changes."
+ "\n",
+ "\n",
+ "To read more about other sklearn classifiers visit [here ðŸ§](https://scikit-learn.org/stable/supervised_learning.html). Try and use other classifiers to see how the performance of your model changes. Try using [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) or [MLP](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) and compare how the performance changes."
]
},
{
@@ -429,34 +238,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/shubh/.local/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
- " FutureWarning)\n",
- "/home/shubh/.local/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
- " \"this warning.\", FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
- " intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
- " multi_class='warn', n_jobs=None, penalty='l2',\n",
- " random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
- " warm_start=False)"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"classifier.fit(X_train, y_train)"
]
@@ -468,17 +252,26 @@
"Got a warning! Dont worry, its just beacuse the number of iteration is very less(defined in the classifier in the above cell).Increase the number of iterations and see if the warning vanishes and also see how the performance changes.Do remember increasing iterations also increases the running time.( Hint: max_iter=500)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Validation Phase 🤔\n",
+ "Wonder how well your model learned! Lets check it."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict on Validation\n",
- "Now we predict our trained classifier on the validation set and evaluate our model"
+ "\n",
+ "Now we predict using our trained model on the validation set we created and evaluate our model on unforeseen data."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -490,13 +283,16 @@
"metadata": {},
"source": [
"## Evaluate the Performance\n",
- "We use the same metrics as that will be used for the test set. \n",
- "[F1 score](https://en.wikipedia.org/wiki/F1_score) and [Log Loss](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) are the metrics for this challenge"
+ "\n",
+ "- We have used basic metrics to quantify the performance of our model. \n",
+ "- This is a crucial step, you should reason out the metrics and take hints to improve aspects of your model.\n",
+ "- Do read up on the meaning and use of different metrics. there exist more metrics and measures, you should learn to use them correctly with respect to the solution,dataset and other factors. \n",
+ "- [F1 score](https://en.wikipedia.org/wiki/F1_score) are the metrics for this challenge"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -508,20 +304,11 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy of the model is : 0.9823529411764705\n",
- "Recall of the model is : 0.9823529411764705\n",
- "Precision of the model is : 0.9823529411764705\n",
- "F1 score of the model is : 0.9652514456902025\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"print(\"Accuracy of the model is :\" ,accuracy)\n",
"print(\"Recall of the model is :\" ,recall)\n",
@@ -533,7 +320,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Prediction on Evaluation Set"
+ "# Testing Phase 😅\n",
+ "\n",
+ "We are almost done. We trained and validated on the training data. Now its the time to predict on test set and make a submission."
]
},
{
@@ -541,16 +330,17 @@
"metadata": {},
"source": [
"## Load Test Set\n",
- "Load the test data now"
+ "\n",
+ "Load the test data on which final submission is to be made."
]
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "final_test_path = \"test.csv\"\n",
+ "final_test_path = \"data/test.csv\"\n",
"final_test = pd.read_csv(final_test_path)"
]
},
@@ -564,7 +354,7 @@
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -580,7 +370,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -589,19 +379,22 @@
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"submission = pd.DataFrame(submission)\n",
- "submission.to_csv('/tmp/submission.csv',header=['NSP'],index=False)"
+ "submission.to_csv('data/submission.csv',header=['NSP'],index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Note: Do take a look at the submission format.The submission file should contain a header."
+ "🚧 Note : \n",
+ "- Do take a look at the submission format. \n",
+ "- The submission file should contain a header. \n",
+ "- Follow all submission guidelines strictly to avoid inconvenience."
]
},
{
@@ -617,23 +410,19 @@
"metadata": {},
"outputs": [],
"source": [
- "from google.colab import files\n",
- "files.download('/tmp/submission.csv') "
+ "try:\n",
+ " from google.colab import files\n",
+ " files.download('submission.csv')\n",
+ "except ImportError as e:\n",
+ " print(\"Only for Collab\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Go to [platform](https://www.aicrowd.com/challenges/bkmkt-bank-marketing-stategy-analysis). Participate in the challenge and submit the submission.csv."
+ "### Well Done! 👠We are all set to make a submission and see your name on leaderborad. Lets navigate to [challenge page](https://www.aicrowd.com/challenges/crdio) and make one."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -652,7 +441,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/baselines/CRDIO_baseline.ipynb b/baselines/CRDIO_baseline.ipynb
index 5ed8c17..3d0ccc2 100644
--- a/baselines/CRDIO_baseline.ipynb
+++ b/baselines/CRDIO_baseline.ipynb
@@ -4,79 +4,56 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Baseline for crdio Educational Challenge on AIcrowd\n",
- "#### Author : Shubham Sharma"
+ ""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## To open this notebook on Google Computing platform Colab, click below!"
+ "# Baseline for [CRDIO](https://www.aicrowd.com/challenges/crdio) Challenge on AIcrowd\n",
+ "#### Author : Shubham Sharma"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "[](https://colab.research.google.com/github/googlecolab/colabtools/blob/master/notebooks/colab-github-demo.ipynb)\n"
+ "## Download Necessary Packages 📚"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"metadata": {},
+ "outputs": [],
"source": [
- "## Download Necessary Packages"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Requirement already satisfied: numpy in /home/ayush/.local/lib/python3.7/site-packages (1.18.1)\n",
- "Requirement already satisfied: pandas in /home/ayush/.local/lib/python3.7/site-packages (0.25.0)\n",
- "Requirement already satisfied: numpy>=1.13.3 in /home/ayush/.local/lib/python3.7/site-packages (from pandas) (1.18.1)\n",
- "Requirement already satisfied: pytz>=2017.2 in /home/ayush/.local/lib/python3.7/site-packages (from pandas) (2019.3)\n",
- "Requirement already satisfied: python-dateutil>=2.6.1 in /home/ayush/anaconda3/lib/python3.7/site-packages (from pandas) (2.8.0)\n",
- "Requirement already satisfied: six>=1.5 in /home/ayush/anaconda3/lib/python3.7/site-packages (from python-dateutil>=2.6.1->pandas) (1.12.0)\n",
- "Requirement already satisfied: scikit-learn in /home/ayush/.local/lib/python3.7/site-packages (0.21.3)\n",
- "Requirement already satisfied: scipy>=0.17.0 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (1.4.1)\n",
- "Requirement already satisfied: joblib>=0.11 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (0.14.0)\n",
- "Requirement already satisfied: numpy>=1.11.0 in /home/ayush/.local/lib/python3.7/site-packages (from scikit-learn) (1.18.1)\n"
- ]
- }
- ],
- "source": [
- "import sys\n",
- "!{sys.executable} -m pip install numpy\n",
- "!{sys.executable} -m pip install pandas\n",
- "!{sys.executable} -m pip install scikit-learn"
+ "!pip install numpy\n",
+ "!pip install pandas\n",
+ "!pip install scikit-learn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Download data\n",
- "The first step is to download out train test data. We will be training a classifier on the train data and make predictions on test data. We submit our predictions\n"
+ "## Download Data\n",
+ "The first step is to download out train test data. We will be training a model on the train data and make predictions on test data. We submit our predictions\n"
]
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "#Donwload the datasets\n",
- "!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/public/crdio/v0.1/test.csv\n",
- "!wget https://s3.eu-central-1.wasabisys.com/aicrowd-public-datasets/public/crdio/v0.1/test.csv"
+ "!rm -rf data\n",
+ "!mkdir data \n",
+ "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/crdio/v0.1/test.csv\n",
+ "!wget https://datasets.aicrowd.com/default/aicrowd-practice-challenges/public/crdio/v0.1/train.csv\n",
+ "!mv test.csv data/test.csv\n",
+ "!mv train.csv data/train.csv\n",
+ "\n"
]
},
{
@@ -89,7 +66,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -107,277 +84,92 @@
"metadata": {},
"source": [
"## Load Data\n",
- "We use pandas library to load our data. Pandas loads them into dataframes which helps us analyze our data easily. Learn more about it [here](https://www.tutorialspoint.com/python_data_science/python_pandas.htm)"
+ "- We use pandas 🼠library to load our data. \n",
+ "- Pandas loads the data into dataframes and facilitates us to analyse the data. \n",
+ "- Learn more about it [here](https://www.tutorialspoint.com/python_data_science/python_pandas.htm) 🤓"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "train_data_path = \"train.csv\" #path where data is stored"
+ "all_data_path = \"data/train.csv\" #path where data is stored"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "train_data = pd.read_csv(train_data_path) #load data in dataframe using pandas"
+ "all_data = pd.read_csv(all_data_path) #load data in dataframe using pandas"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Visualize the data"
+ "## Visualize the data 👀"
]
},
{
"cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>LBE</th>\n",
- " <th>LB</th>\n",
- " <th>AC</th>\n",
- " <th>FM</th>\n",
- " <th>UC</th>\n",
- " <th>ASTV</th>\n",
- " <th>MSTV</th>\n",
- " <th>ALTV</th>\n",
- " <th>MLTV</th>\n",
- " <th>DL</th>\n",
- " <th>...</th>\n",
- " <th>B</th>\n",
- " <th>C</th>\n",
- " <th>D</th>\n",
- " <th>E</th>\n",
- " <th>AD</th>\n",
- " <th>DE</th>\n",
- " <th>LD</th>\n",
- " <th>FS</th>\n",
- " <th>SUSP</th>\n",
- " <th>NSP</th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>0</th>\n",
- " <td>135.0</td>\n",
- " <td>135.0</td>\n",
- " <td>8.0</td>\n",
- " <td>2.0</td>\n",
- " <td>7.0</td>\n",
- " <td>60.0</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>5.2</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>1</th>\n",
- " <td>133.0</td>\n",
- " <td>133.0</td>\n",
- " <td>0.0</td>\n",
- " <td>4.0</td>\n",
- " <td>6.0</td>\n",
- " <td>60.0</td>\n",
- " <td>2.5</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>4.0</td>\n",
- " <td>...</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>135.0</td>\n",
- " <td>135.0</td>\n",
- " <td>10.0</td>\n",
- " <td>2.0</td>\n",
- " <td>4.0</td>\n",
- " <td>60.0</td>\n",
- " <td>0.9</td>\n",
- " <td>0.0</td>\n",
- " <td>0.5</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>3</th>\n",
- " <td>120.0</td>\n",
- " <td>120.0</td>\n",
- " <td>3.0</td>\n",
- " <td>1.0</td>\n",
- " <td>2.0</td>\n",
- " <td>56.0</td>\n",
- " <td>0.5</td>\n",
- " <td>0.0</td>\n",
- " <td>8.8</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>4</th>\n",
- " <td>132.0</td>\n",
- " <td>132.0</td>\n",
- " <td>6.0</td>\n",
- " <td>0.0</td>\n",
- " <td>6.0</td>\n",
- " <td>27.0</td>\n",
- " <td>1.7</td>\n",
- " <td>0.0</td>\n",
- " <td>11.3</td>\n",
- " <td>0.0</td>\n",
- " <td>...</td>\n",
- " <td>1.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>0.0</td>\n",
- " <td>1</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>5 rows × 34 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " LBE LB AC FM UC ASTV MSTV ALTV MLTV DL ... B C \\\n",
- "0 135.0 135.0 8.0 2.0 7.0 60.0 1.0 0.0 5.2 0.0 ... 1.0 0.0 \n",
- "1 133.0 133.0 0.0 4.0 6.0 60.0 2.5 0.0 0.0 4.0 ... 0.0 0.0 \n",
- "2 135.0 135.0 10.0 2.0 4.0 60.0 0.9 0.0 0.5 0.0 ... 1.0 0.0 \n",
- "3 120.0 120.0 3.0 1.0 2.0 56.0 0.5 0.0 8.8 0.0 ... 1.0 0.0 \n",
- "4 132.0 132.0 6.0 0.0 6.0 27.0 1.7 0.0 11.3 0.0 ... 1.0 0.0 \n",
- "\n",
- " D E AD DE LD FS SUSP NSP \n",
- "0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 \n",
- "1 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1 \n",
- "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 \n",
- "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 \n",
- "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1 \n",
- "\n",
- "[5 rows x 34 columns]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "train_data.head()"
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_data.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "The dataset consists of `34` attributes out of which first `33` attributes describes details of `CTGs` features and last attribute called `NSP` is used to classify these `CTGs` on the basis of fetal state."
+ "The dataset consists of `24` attributes out of which first `23` attributes describes details of `CTGs` features and last attribute called `NSP` is used to classify these `CTGs` on the basis of fetal state."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Split Data into Train and Validation\n",
- "Now we want to see how well our classifier is performing, but we dont have the test data labels with us to check. What do we do ? So we split our dataset into train and validation. The idea is that we test our classifier on validation set in order to get an idea of how well our classifier works. This way we can also ensure that we dont [overfit](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) on the train dataset. There are many ways to do validation like [k-fold](https://machinelearningmastery.com/k-fold-cross-validation/),[leave one out](https://en.wikipedia.org/wiki/Cross-validation_(statistics), etc"
+ "## Split Data into Train and Validation 🔪\n",
+ "- The next step is to think of a way to test how well our model is performing. we cannot use the test data given as it does not contain the data labels for us to verify. \n",
+ "- The workaround this is to split the given training data into training and validation. Typically validation sets give us an idea of how our model will perform on unforeseen data. it is like holding back a chunk of data while training our model and then using it to for the purpose of testing. it is a standard way to fine-tune hyperparameters in a model. \n",
+ "- There are multiple ways to split a dataset into validation and training sets. following are two popular ways to go about it, [k-fold](https://machinelearningmastery.com/k-fold-cross-validation/), [leave one out](https://en.wikipedia.org/wiki/Cross-validation_statistics). ðŸ§\n",
+ "- Validation sets are also used to avoid your model from [overfitting](https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/) on the train dataset."
]
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "X_train, X_val= train_test_split(train_data, test_size=0.2, random_state=42) "
+ "X_train, X_val= train_test_split(all_data, test_size=0.2, random_state=42) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Here we have selected the size of the testing data to be 20% of the total data. You can change it and see what effect it has on the accuracies. To learn more about the train_test_split function [click here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html)."
+ "- We have decided to split the data with 20 % as validation and 80 % as training. \n",
+ "- To learn more about the train_test_split function [click here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html). 🧠\n",
+ "- This is of course the simplest way to validate your model by simply taking a random chunk of the train set and setting it aside solely for the purpose of testing our train model on unseen data. as mentioned in the previous block, you can experiment 🔬 with and choose more sophisticated techniques and make your model better."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Now, since we have our data splitted into train and validation sets, we need to get the label separated from the data."
+ "- Now, since we have our data splitted into train and validation sets, we need to get the corresponding labels separated from the data. \n",
+ "- with this step we are all set move to the next step with a prepared dataset."
]
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -389,35 +181,52 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## Define the Classifier\n",
- "Now we come to the juicy part. We have fixed our data and now we train a classifier. The classifier will learn the function by looking at the inputs and corresponding outputs. There are a ton of classifiers to choose from some being [Logistic Regression](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc), [SVM](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Random Forests](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Decision Trees](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052), etc. \n",
- "Tip: A good model doesnt depend solely on the classifier but on the features(columns) you choose. So make sure to play with your data and keep only whats important. "
+ "# TRAINING PHASE ðŸ‹ï¸"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Define the Model\n",
+ "\n",
+ "- We have fixed our data and now we are ready to train our model. \n",
+ "\n",
+ "- There are a ton of classifiers to choose from some being [Logistic Regression](https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc), [SVM](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Random Forests](https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47), [Decision Trees](https://towardsdatascience.com/decision-trees-in-machine-learning-641b9c4e8052), etc.🧠\n",
+ "\n",
+ "- Remember that there are no hard-laid rules here. you can mix and match classifiers, it is advisable to read up on the numerous techniques and choose the best fit for your solution , experimentation is the key. \n",
+ " \n",
+ "- A good model does not depend solely on the classifier but also on the features you choose. So make sure to analyse and understand your data well and move forward with a clear view of the problem at hand. you can gain important insight from [here](https://towardsdatascience.com/the-5-feature-selection-algorithms-every-data-scientist-need-to-know-3a6b566efd2).🧠"
]
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "# classifier = SVC(gamma='auto')\n",
+ "classifier = SVC(gamma='auto')\n",
"\n",
- "#from sklearn.linear_model import LogisticRegression\n",
- "classifier = LogisticRegression()"
+ "# from sklearn.linear_model import LogisticRegression\n",
+ "# classifier = LogisticRegression()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We have used [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html#classification) as a classifier here and set few of the parameteres. But one can set more parameters and increase the performance. To see the list of parameters visit [here](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html)."
+ "- To start you off, We have used a basic [Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html#classification) classifier here. \n",
+ "- But you can tune parameters and increase the performance. To see the list of parameters visit [here](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html). \n",
+ "- Do keep in mind there exist sophisticated techniques for everything, the key as quoted earlier is to search them and experiment to fit your implementation."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "We can also use other classifiers. To read more about sklean classifiers visit [here](https://scikit-learn.org/stable/supervised_learning.html). Try and use other classifiers to see how the performance of your model changes. Try using [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) or [MLP](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) and compare how the performance changes."
+ "\n",
+ "\n",
+ "To read more about other sklearn classifiers visit [here ðŸ§](https://scikit-learn.org/stable/supervised_learning.html). Try and use other classifiers to see how the performance of your model changes. Try using [Logistic Regression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) or [MLP](http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html) and compare how the performance changes."
]
},
{
@@ -429,34 +238,9 @@
},
{
"cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "/home/shubh/.local/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
- " FutureWarning)\n",
- "/home/shubh/.local/lib/python3.5/site-packages/sklearn/linear_model/logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
- " \"this warning.\", FutureWarning)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
- " intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
- " multi_class='warn', n_jobs=None, penalty='l2',\n",
- " random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
- " warm_start=False)"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
"source": [
"classifier.fit(X_train, y_train)"
]
@@ -468,17 +252,26 @@
"Got a warning! Dont worry, its just beacuse the number of iteration is very less(defined in the classifier in the above cell).Increase the number of iterations and see if the warning vanishes and also see how the performance changes.Do remember increasing iterations also increases the running time.( Hint: max_iter=500)"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Validation Phase 🤔\n",
+ "Wonder how well your model learned! Lets check it."
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predict on Validation\n",
- "Now we predict our trained classifier on the validation set and evaluate our model"
+ "\n",
+ "Now we predict using our trained model on the validation set we created and evaluate our model on unforeseen data."
]
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -490,13 +283,16 @@
"metadata": {},
"source": [
"## Evaluate the Performance\n",
- "We use the same metrics as that will be used for the test set. \n",
- "[F1 score](https://en.wikipedia.org/wiki/F1_score) and [Log Loss](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.log_loss.html) are the metrics for this challenge"
+ "\n",
+ "- We have used basic metrics to quantify the performance of our model. \n",
+ "- This is a crucial step, you should reason out the metrics and take hints to improve aspects of your model.\n",
+ "- Do read up on the meaning and use of different metrics. there exist more metrics and measures, you should learn to use them correctly with respect to the solution,dataset and other factors. \n",
+ "- [F1 score](https://en.wikipedia.org/wiki/F1_score) are the metrics for this challenge"
]
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -508,20 +304,11 @@
},
{
"cell_type": "code",
- "execution_count": 16,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Accuracy of the model is : 0.9823529411764705\n",
- "Recall of the model is : 0.9823529411764705\n",
- "Precision of the model is : 0.9823529411764705\n",
- "F1 score of the model is : 0.9652514456902025\n"
- ]
- }
- ],
+ "execution_count": null,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
"source": [
"print(\"Accuracy of the model is :\" ,accuracy)\n",
"print(\"Recall of the model is :\" ,recall)\n",
@@ -533,7 +320,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "# Prediction on Evaluation Set"
+ "# Testing Phase 😅\n",
+ "\n",
+ "We are almost done. We trained and validated on the training data. Now its the time to predict on test set and make a submission."
]
},
{
@@ -541,16 +330,17 @@
"metadata": {},
"source": [
"## Load Test Set\n",
- "Load the test data now"
+ "\n",
+ "Load the test data on which final submission is to be made."
]
},
{
"cell_type": "code",
- "execution_count": 117,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
- "final_test_path = \"test.csv\"\n",
+ "final_test_path = \"data/test.csv\"\n",
"final_test = pd.read_csv(final_test_path)"
]
},
@@ -564,7 +354,7 @@
},
{
"cell_type": "code",
- "execution_count": 118,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -580,7 +370,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -589,19 +379,22 @@
},
{
"cell_type": "code",
- "execution_count": 119,
+ "execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"submission = pd.DataFrame(submission)\n",
- "submission.to_csv('/tmp/submission.csv',header=['NSP'],index=False)"
+ "submission.to_csv('data/submission.csv',header=['NSP'],index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "Note: Do take a look at the submission format.The submission file should contain a header."
+ "🚧 Note : \n",
+ "- Do take a look at the submission format. \n",
+ "- The submission file should contain a header. \n",
+ "- Follow all submission guidelines strictly to avoid inconvenience."
]
},
{
@@ -617,23 +410,19 @@
"metadata": {},
"outputs": [],
"source": [
- "from google.colab import files\n",
- "files.download('/tmp/submission.csv') "
+ "try:\n",
+ " from google.colab import files\n",
+ " files.download('submission.csv')\n",
+ "except ImportError as e:\n",
+ " print(\"Only for Collab\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "### Go to [platform](https://www.aicrowd.com/challenges/bkmkt-bank-marketing-stategy-analysis). Participate in the challenge and submit the submission.csv."
+ "### Well Done! 👠We are all set to make a submission and see your name on leaderborad. Lets navigate to [challenge page](https://www.aicrowd.com/challenges/crdio) and make one."
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
@@ -652,7 +441,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.5.2"
+ "version": "3.7.6"
}
},
"nbformat": 4,
diff --git a/baselines/train.csv b/baselines/train.csv
deleted file mode 100644
index ec7cef5..0000000
--- a/baselines/train.csv
+++ /dev/null
@@ -1,1701 +0,0 @@
-LBE,LB,AC,FM,UC,ASTV,MSTV,ALTV,MLTV,DL,DS,DP,DR,Width,Min,Max,Nmax,Nzeros,Mode,Mean,Median,Variance,Tendency,NSP
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