+ Time Limit: 1 second
+ Memory Limit: 256 MB
----------
In this problem, you are given two integers $a$ and $b$. Write a program that reads $a$ and $b$ and prints $a + b$.
# Input
The input consists of a single line containing two integers $a$ and $b$, separated by a space.
$$1 \leq a, b \leq 100$$
# Output
Print the value of $a + b$ in a single line.
# Examples
### Sample Input 1
```
3 5
```
### Sample Output 1
```
8
```
### Sample Input 2
```
1 1
```
### Sample Output 2
```
2
```
Summation
| You can download the initial question file from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305557/). |
| :--: |
Amirali's parents promised to give him a bicycle if he gets a high average in his exams. But Amirali can't wait until the end of the exams and wants to know right now how much of a chance he has to get a high average. That's why he asks you to train a model that estimates the final average of a student using their information throughout the school year.
## Evaluation Metric
The R2 Score metric is used to evaluate your model. To score in this question, your model must have an R2 Score of at least 0.4, and in this case, the final score will be calculated based on the following formula:
```
round(r2score,3)×100
```
If your model does not reach the minimum threshold, the score you receive will be **zero**.
<details class="red"><summary>**Attention**</summary>
During the competition, the score you see is only the result of evaluating your model on 30% of the test data. After the competition time ends, your **final score** will be calculated on the remaining 70%.
This is done to prevent overfitting and maintain the generality of the model to ensure that models that have been overfitted will have their scores reduced in the final scoring.
</details>
## How to Submit a Response
To answer this question, first open the notebook file located in the initial file, and then perform the steps as requested. Finally, after running the answer-generating cell (the last cell of the notebook file), submit the created result.zip file.
<details class="red"><summary>**Important Warning**</summary>
Note that before running the answer-generating cell, you must have saved the changes made in the notebook using the ctrl+s shortcut key, otherwise, at the end of the competition, your **score** will be changed to **zero**.
Also, if you are using Colab to run this notebook file, before submitting the result.zip file, download the latest version of your notebook and place it inside the submitted file.
</details>
Grade Point Averaging
| You can download the initial file for this question from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305559/). |
| :--: |
In large cities like Los Angeles, understanding and predicting criminal activities is vital for improving public safety and optimizing resource allocation. In these areas, law enforcement agencies collect a large volume of data related to criminal incidents. This data typically includes information such as the date, time, and location of the incident, victim demographics, the type of weapon used, and the case status. By analyzing this data, patterns and trends in criminal behavior can be identified. The goal of this problem is to develop a machine learning model that can predict the type of crime based on various incident details.
## Evaluation Criteria
To evaluate your model, the F1 Score metric is used, and the averaging model is macro.To score in this question, your model must have a minimum F1 Score of 0.25, and in this case, the final score will be calculated based on the following formula:
```
round(f1score,3)×250
```
If your model does not meet the threshold, the score received will be **zero**.
<details class="red">
<summary>
**Attention**
</summary>
During the competition, the score you see is only the result of your model's evaluation on 30% of the test data. After the competition time ends, your **final score** will be calculated on the remaining 70%.
This is done to prevent overfitting and to maintain the generality of the model, to ensure that models that have overfitted will drop in the final scoring.
</details>
## How to Submit Your Answer
To answer this question, first open the notebook file located in the initial file and then follow the steps as requested. Finally, after running the answer-generating cell (the last cell of the notebook file), submit the created result.zip file.
<details class="red">
<summary>
**Important Warning**
</summary>
Please note that before running the answer-generating cell, you must have saved the changes made in the notebook using the ctrl+s shortcut, otherwise, at the end of the competition, your **score** will be changed to **zero**.
Also, if you are using Colab to run this notebook file, before submitting the result.zip file, download the latest version of your notebook and place it inside the submitted file.
</details>
Criminology
| You can download the initial file for this question from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305563/).|
| :--: |
Amiral started a research project on human physical activities in the first steps of his company. In this project, motion sensors have been attached to the right thigh and lower back of a number of volunteers, and these sensors have recorded the acceleration data of these two areas for 2 hours. Now Amiral wants to implement a model that can detect the individual's movement status (walking, running, etc.) with this data. For this, he has labeled the data of one of the volunteers from the beginning of the sensor attachment up to a specific time, and asks you to design a model that predicts the time from that point onward.
## Evaluation Metric
The `F1 Score` metric is used to evaluate your model, and the averaging method is `macro`.
To score in this question, your model must have an `F1 Score` of at least 0.40, and in this case, the final score is calculated based on the following formula:
$$round(f1score, 3) \times 100$$
If your model does not reach the minimum threshold, the received score will be **zero**.
<details class="red">
<summary>
**Attention**
</summary>
During the competition, the score you observe is only the result of your model's evaluation on 30% of the test data. After the competition time ends, your **final score** will be calculated on the remaining 70%.
This is done to prevent `overfitting` and maintain the generality of the model to ensure that models that have been overfitted will drop in the final scoring.
</details>
## How to Submit the Answer
To answer this question, first open the notebook file included in the initial file and then follow the steps as requested. Finally, after executing the answer-generating cell (the last cell of the notebook file), submit the created `result.zip` file.
<details class="red">
<summary>
**Important Warning**
</summary>
Note that before executing the answer-generating cell, save the changes made in the notebook using the shortcut key `ctrl+s`; otherwise, your **score** will change to **zero** at the end of the competition.
Also, if you are using Colab to run this notebook file, before submitting the `result.zip` file, download the latest version of your notebook and include it in the submission file.
</details>
Movement Status
| You can download the initial problem file from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305561/).|
| :--: |
Regarding the prediction of the cryptocurrency market trend, it can be said that this trend depends on multiple factors, including factors related to global markets and daily information and news. Technical analyses (which examine price patterns and indicators) and fundamental analyses (which examine economic and political news and events) are two important methods for predicting crypto markets.
Note that due to their dynamism and the influence of unpredictable factors such as news, government decisions, and important global events, cryptocurrency markets usually have more irregularity in their trends compared to traditional markets. Therefore, **market trend predictions**, although they can provide useful information, must be used with caution and by considering the risks of dynamic crypto markets.
In this question, we want to examine the impact of existing news on a cryptocurrency. To examine these cases, we have provided you with the headlines of the news and their effects on a cryptocurrency in a specific time frame. And we ask you to predict the `Label` column for the coming days.
# Dataset
The dataset provided to you in this question includes the following columns:
| Column | Description |
|:-------------------:|:----------------------:|
| `Date` | Trading Day Date |
| `Top 1-25` | News headlines related to that day|
| `Label` | Number 0 or 1 |
+ **Note:** If the adjusted price on the trading day is higher than the previous day, the value of the `Label` column will be `1`, and if it is lower than the previous day, it will be `0`.
# Final Requirement
In this question, you must predict the value of the `Label` column for the next 400 trading days using machine learning models.
# Evaluation
Your model will be evaluated using the `F1 Score` metric, and the averaging model is `Weighted`.
<details class="red">
<summary>
**Attention**
</summary>
During the competition, the score you see is only the result of the `F1 Score` on 30% of the file you upload to Quera. After the competition time ends, your **final score** will be calculated on the remaining 70%.
This is done to prevent `overfitting` and maintain the generality of the model to ensure that models that have been overfitted will have a lower final score.
</details>
# Question Output
You must save your predictions **in order** in a column named `prediction` as a `csv` file named `submission.csv` that contains one column named `prediction`.
## Sample Output
| `prediction` |
|:---------------:|
| 0 |
| 1 |
| 0 |
| 1 |
| 0 |
<details class="yellow">
<summary>
**Important Notes About the Submission File**
</summary>
+ **Note 1**: Make sure that the mentioned column definitely has a `header`.
+ **Note 2**: Be careful not to save the index in the final file and only have one `prediction` column.
+ **Note 3**: The numbers in the `prediction` column are merely for example and are not the correct answer!
</details>
<details class="red">
<summary>
**Warning**
</summary>
Do not forget that **before the end of the competition time**, you **must** send us all the codes for this competition from the **Code Upload** section. Otherwise, you will not get any points from this competition.
Note that if you are using a `jupyter notebook`, you must get the `.py` output just like the explanations in the **Code Upload** section and consider it for submission. Submitting `jupyter` files like `.ipynb` is not acceptable.
</details>
Price Prediction Based on News
| You can download the initial file for this question from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305560/).|
| :--: |
Amirali, who has recently graduated from university, wants to start a new startup, but he is very afraid of failure. For this reason, he has researched it extensively, the result of which is a dataset that includes information on a large number of successful or failed startups. Now, Amirali asks you to design a model using this dataset that predicts whether this startup will succeed or not, based on the data from its first few months.
## Evaluation Metric
The `F1 Score` metric is used to evaluate your model, and the averaging method is `macro`.
To score in this question, your model must have an `F1 Score` of at least 0.40, and in this case, the final score will be calculated based on the following formula:
$$round(f1score, 3) \times 100$$
If your model does not reach the threshold, the received score will be **zero**.
<details class="red">
<summary>
**Attention**
</summary>
The score you see during the competition is only the result of your model's evaluation on 30% of the test data. After the competition time ends, your **final score** will be calculated on the remaining 70%.
This is done to prevent overfitting and maintain the generality of the model to ensure that models which have been overfitted will drop in the final scoring.
</details>
## Submission Method
To answer this question, first open the notebook file located in the initial file and then follow the steps as requested. Finally, after running the answer-generating cell (the last cell of the notebook file), submit the created `result.zip` file.
<details class="red">
<summary>
**Important Warning**
</summary>
Note that before running the answer-generating cell, save the changes made in the notebook using the shortcut key `ctrl+s`, otherwise, at the end of the competition, your **score** will change to **zero**.
Also, if you are using Colab to run this notebook file, before submitting the `result.zip` file, download the latest version of your notebook and place it inside the submitted file.
</details>
Successful Startup
| You can download the initial question file from [this link](https://codern.org/contest/assignments/87888/download_problem_initial_project/305562/).|
| :--: |
A lodging booking website has asked for your help to determine if the accommodation registered on its site is being rented at a reasonable price. For this purpose, they provide you with a dataset of information about the accommodations registered on their site so far, and they ask you to train a model that can predict the price of an accommodation based on its specifications.
## Evaluation Criteria
The `R2 Score` metric is used to evaluate your model. To receive a score in this question, your model must have an `R2 Score` of at least 0.4, and in this case, the final score will be calculated based on the following formula:
$$round(r2score, 3) \times 250$$
If your model does not reach the threshold, the received score will be **zero**.
<details class="red">
<summary>
**Attention**
</summary>
During the competition, the score you see is only the result of your model's evaluation on 30 percent of the test data. After the competition time ends, your **final score** will be calculated on the remaining 70 percent.
This is done to prevent overfitting (`overfitting`) and maintain the generality of the model to ensure that models that have been overfitted will have a lower final score.
</details>
## How to Submit the Answer
To answer this question, first open the notebook file provided in the initial file and then follow the steps as requested. Finally, after running the answer-generating cell (the last cell of the notebook file), submit the created `result.zip` file.
<details class="red">
<summary>
**Important Warning**
</summary>
Please note that before running the answer-generating cell, you must have saved the changes made in the notebook using the shortcut key `ctrl+s`, otherwise, at the end of the competition, your **score** will change to **zero**.
Also, if you are using Colab to run this notebook file, before submitting the `result.zip` file, download the latest version of your notebook and place it inside the submission file.
</details>