CodeSprint-Day4-AI-Resume-Analyzer

# Intellifotainment assist” – Smart HMI for passenger cars To run the program, download all files and save them in the same directory. After that, simply run 'Master.py' in the terminal. At the moment, the program will only run in linux based systems. # The Idea Infotainment systems have come a long way since the first set of dashboards installed in cars. Through our idea, we aim to create a Human Machine interaction model that takes infotainment systems to a new level. The driver tends to get distracted from the road while performing secondary tasks such as changing the music track, locking/unlocking the door while driving etc. Our system shall enable the driver to focus only on driving. Controlling the secondary tasks will be much easier. Our product primarily comprises of 5 modules: 1) Attention and drowsiness detection: - A camera shall be present on the dashboard, in front of the driver, behind the steering wheel. Through digital image processing techniques , using hough circle algorithm and haarcascade of an eye, we shall keep track of the driver’s sight. If he or she is looking away from the road while driving for more than a specified amount of time, we shall alert the driver to focus. We shall map the head orientation and iris position to accurately identify the driver’s attention. 2) Infotainment control features using blink combination: Through a combination of blinks, the driver can turn on or of the headlights, tail lights as well as indicators. Blinking of the eyes shall be detected using ‘dlib’ features in python. This shall give extremely accurate results. 3) Voice commands to control wipers, car lock, music system and windows A simple, yet extremely useful idea that would make the life of the driver a whole lot easy. Enabling the driver to speak to his car infotainment system would allow him to control and navigate these functionalities with great ease. The car will be enabled with a virtual assistant. 4) Automatic rear view mirror adjustment scheme: Using the camera placed in front of the driver, the system shall detect the position of the driver’s head. This shall also be done using image processing techniques and we shall identify the coordinates of the driver head in 3D space. There will be a mapping between the head position and mirror adjustment scheme. The mirrors will adjust their position using servo motors and shall do so automatically by identifying the head position. 5) A revolutionary reverse-assistance algorithm for smart parking and general reversing: Probably the highlight of our model, this feature shall make driving the car in and out of a parking spot, or rather, even reversing a car in general, far easier and safer than what it already is. Like most other modern cars, our model shall also have a camera installed at the back and the corresponding image displayed at the infotainment screen for parking assistance. Upon activating the reverse gear, the screen shall trace the line of motion of the car corresponding to the current position of the steering wheel. Because of this feature, the driver gets an idea of whether or not he’ll hit an obstacle while reversing if the steering wheel is kept at that position. Taking this feature to another level, the rear camera, after capturing the live video feed from the back of the car, shall perform image processing and machine learning algorithms to find a safe, obstacle-free path for reversing and indicate the driver to move the steering wheel accordingly. So instead of relying solely on the drivers judgement, our system shall actually find the path to be taken while reversing, such that other cars and other obstacles will be avoided, and accordingly recommend the driver to steer the wheel in that direction. This feature shall be extremely useful for new drivers/ learners. During the initial phase, to prevent errors from creeping in, we will always have a manual override button. After a good amount of testing, further modifications and refinements can be made. Our systems adds new dimensions to both precautionary safety measures, as well as convenience. If implemented properly, we are confident that our project will reach new heights of HMI and driver assistance technology. It will give drivers several less reasons to worry about.

Basically it's open cv python project where user can draw any alphabets or number in the frame using his index finger tip and after that our model can recognize which alphabets or number is written.

PYTHON CODE WALKTHROUGH Data Sourcing In order to run a discounted cash flow model (DCF), I needed data, so I found a free API that provided us with everything I needed. I wrote a code that saved every financial statement of every company in a separate text file. In this code, I asked to ping the API’s URL for every ticker, open a text file for one of the financial statements for one company ticker, dump all the data found by the code into this file, and close it. This process was repeated for every company in our company list and every statement I have a code for. By doing so I Ire able to store the data for every company locally and did not need to ping the API every time I ran our code. Once all the financial data for each company was stored in form of a balance sheet, income statement, cash flow statement, and company profile text file, I needed to pick out specific items required for our DCF model. Thus, I defined the functions that selected all required items from the respective financial statements of each company and assigned them to a variable using utils.py. Discounted Cash Flow Model First of all, I needed to import the functions I defined in utils.py before defining the DCF model function, which would run for every company in our list. Next, I ensured to have 5 consecutive years of past data to compute the average. Thus, the first few lines of code checked whether the last year on record was 2019 from which point I would go back 5 years; if the last year was 2018, this would be taken as the first data entry from which I would go back 5 years. The second part mentioned above is important because companies file their 10-K, i.e. their annual report, at different times throughout the year so there may be companies that already filed their reports while others had not. After this step, five-year averages of every item’s percentage of revenue Ire calculated as Ill as the average revenue growth over the same period. These items included EBIT, depreciation & amortization, capital expenditures, and the change in net working capital. Once that was done, there Ire only three variables missing before calculating free cash flows for the next few years: a discount or hurdle rate; industry-specific perpetual growth rates; and a tax rate. After these three variables Ire set up, the next step was to calculate the free cash flows to the firm (fcff) for the next 5 years and determine the terminal value at the end of the period using the growth rate for the corresponding industry. For the former, I use a loop to calculate the fcff for all the year, discount it, and add it to one variable called fcffpv. Once the terminal value was calculated, these two additional numbers captured the enterprise value of the firm. Since I Ire interested in the equity value, I subtracted debt and add cash, which left us with the equity value. In one final step, I divided this value by the number of shares to end up with an intrinsic value per share. After calculating the intrinsic value per share, I compared it to the current share price with two additions. First, I added a buffer to minimize our downside risk for inaccuracy in calculations, which is called the margin of safety. Here, the intrinsic value should at least be 115% of the current share price. I also set an upper limit at 130% to ensure I would not include companies with extraordinarily high valuations, compared to their current price. If the share price calculated fell within this window, I added its ticker to a dataframe, which was the last step in the function. As such, the DCF function would run for every company and provide a dataframe with the tickers of all those companies that Ire undervalued at the time and fell within the 115% - 130% range. Portfolio Optimization The dataframe with the tickers of all the undervalued companies that was previously created has now become the portfolio, which I converted into a list and used as the source for further optimization that is about to come. Some general inputs for the rest of the code Ire the start and end date of the data I requested for optimization, as Ill as the risk-free rate and the number of simulations I wanted to run our optimizations for. Now that the general framework has been created, it is time to choose some conditioning variables to measure the performance of investment in one sector or across a combination of some/all sectors, respectively. Project Alpha uses the following conditioning variables to optimize its portfolios: • Sharpe Ratio: It measures the performance of an investment compared to the risk-free asset, i.e. the 10-year Treasury Bond, after adjusting for its risk factor or standard deviation. The Sharpe ratio would be given a higher Iight for investors who have a higher risk tolerance. In terms of code, I used the bt package to retrieve the data betIen the predetermined start and end date for the companies in our ticker list. This data was then used to find the portfolio with the highest Sharpe ratio. For that, random Iights Ire assigned to each company and the ratio was computed. After running the number of simulations previously determined, the Iights with the highest Sharpe ratio will be located using loc() and labeled ‘sharpe_portfolio’ which is a dataframe containing the excess return, the volatility, Sharpe ratio, as Ill as the Iights for every company. I also located the portfolio with the loIst volatility, put it in a dataframe called ‘min_volatility_port’ which has the same attributes. The rest of the code of this segment simply created a picture with all the portfolios generated, displaying the efficient frontier and highlighting the portfolio with the highest Sharpe ratio and loIst volatility. • Value at Risk (VaR): VaR was chosen as a diagnostic tool to assess the model. In our case, it basically indicated the percentage of time in which a loss greater than 1% would occur over a period of 5 years. Its limitation is that although it measures how bad the best of the bad is, it does not measure how bad it can get, meaning the worst of the worst. In regards to the code, I first requested the adjusted closing for the companies in our ticker list in the determined time horizon. I then retrieved the Iights from our Sharpe portfolio, set the number of days I wanted to simulate as Ill as the cutoff, before calculating the returns of every company in every period; here: daily. Thereafter, I created a new variable called ‘sigma’, which was be a copy of our return variable, in order to ensure the right format and type for our Monte Carlo loop. The simulation is pretty straight forward, as it measures how many runs the returns fall within 1% or outside of it. I then Iighed the resulting returns by the Iight of the company in the portfolio and whenever the portfolio return was outside the set boundary, it would count as a ‘bad simulation’. Once that is done, the number of bad simulations was divided by the total number of simulations to end up with a percentage of how many simulations were bad, which equals our VaR • Treynor Ratio: For the investors that already have a perfectly diversified portfolio and would like to add more assets to it, there would be a higher Iight on the Treynor ratio. It basically uses beta as a risk factor because it carries the risk relative to the market, instead of standard deviation as in Sharpe, meaning only systematic or non-diversifiable risk. For the code, I first calculated the portfolio’s beta. For that, I defined a function ‘beta’ that reads the beta of every company and returns it. The next step is to run a loop that would enter the beta of every company in our ticker list into a new dataframe. After setting the index equal to the tickers and transposing the Sharpe portfolio Iights, I can concat the two thus resulting in two columns: one is the beta of every company and the second is the corresponding Iight in the portfolio. I then created a third column as the product of columns one and two. The sum of all entries in that column is the portfolio beta, which was then used as the denominator for the ratio. The nominator was already calculated as ‘Excess Return’ in the Sharpe portfolio. • Sortino Ratio: The Sortino ratio measures only the downside risk (downside deviation or semi-deviation) by measuring returns against a minimum acceptable return, 𝜏. It is surprising to know that most of the industry ignores the total number of periods taken and just calculates the downside deviation by choosing the periods with downside risk, which results in misleading results. Project Alpha uses all the periods to calculate the same, so as to have an advantage over those robo-advisors/financial advisors that do not follow this process. The alpha in the future would be generated by going long on companies with high correct Sortino and low incorrect Sortino as they are undervalued, and shorting those with low correct Sortino and high incorrect Sortino as these are overvalued. The Sortino ratio would be given more Iight for investors who are more risk averse. This part of the code started with retrieving the data for our benchmark, the S&P 500, for the period and the calculating the average daily and annual return. After that, I calculate the portfolio returns, ‘returns[“Returns”]’, by adding the products of every company’s Iight times its return, which gave us the portfolio return for every period. From here, I calculated the downside risk by comparing the portfolio return in every period to the daily average return of our benchmark in a for loop. Before I did that, I defined a new variable called ‘semi’, which is a data series and will be filled with whatever comes out of the loop every single time. If the portfolio return minus the average daily return of the benchmark was greater than 0 – meaning the portfolio earned more than the average of the S&P500 – the value for the period was set to 0 and added to the semi data series. If it is 0, which is extremely unlikely, but whatever, it would also be 0. If it is less than 0, hoIver, which indicates underperformance, I would square the portfolio return, which already gives us the semi variance I need for our next step. From here, I can simply take the square root of the average of the ‘semi’ data series to get the daily downside risk and multiplying it by the square root of 252, which gives us the annual number. After that, I have all the numbers to calculate the Sortino ratio. • Information Ratio: The information ratio measures the portfolio returns compared to the returns of a benchmark index, i.e. S&P500, after adjusting for its additional risk. It only looks at the excess return of the portfolio over the benchmark and the volatility or risk associated with it. I already have all the inputs I need to calculate his ratio. Thus, I simply created a new dataframe with the portfolio returns of every period and the benchmark returns of every period. To find the excess return, i.e. the nominator, I simply subtracted the latter from the former and assigned it to a new variable, which I called ‘excess_return’. The nominator would be the average return of the portfolio minus the average return of the benchmark, and the denominator would be the standard deviation of the ‘excess_return’ series. Finally, I printed short sentences with the results for every conditioning variable just described as an output in the console.

Language Translation In this project, you’re going to take a peek into the realm of neural network machine translation. You’ll be training a sequence to sequence model on a dataset of English and French sentences that can translate new sentences from English to French. Get the Data Since translating the whole language of English to French will take lots of time to train, we have provided you with a small portion of the English corpus. """ DON'T MODIFY ANYTHING IN THIS CELL """ import helper import problem_unittests as tests source_path = 'data/small_vocab_en' target_path = 'data/small_vocab_fr' source_text = helper.load_data(source_path) target_text = helper.load_data(target_path) Explore the Data Play around with view_sentence_range to view different parts of the data. view_sentence_range = (0, 10) """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np print('Dataset Stats') print('Roughly the number of unique words: {}'.format(len({word: None for word in source_text.split()}))) sentences = source_text.split('\n') word_counts = [len(sentence.split()) for sentence in sentences] print('Number of sentences: {}'.format(len(sentences))) print('Average number of words in a sentence: {}'.format(np.average(word_counts))) print() print('English sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(source_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) print() print('French sentences {} to {}:'.format(*view_sentence_range)) print('\n'.join(target_text.split('\n')[view_sentence_range[0]:view_sentence_range[1]])) Dataset Stats Roughly the number of unique words: 227 Number of sentences: 137861 Average number of words in a sentence: 13.225277634719028 English sentences 0 to 10: new jersey is sometimes quiet during autumn , and it is snowy in april . the united states is usually chilly during july , and it is usually freezing in november . california is usually quiet during march , and it is usually hot in june . the united states is sometimes mild during june , and it is cold in september . your least liked fruit is the grape , but my least liked is the apple . his favorite fruit is the orange , but my favorite is the grape . paris is relaxing during december , but it is usually chilly in july . new jersey is busy during spring , and it is never hot in march . our least liked fruit is the lemon , but my least liked is the grape . the united states is sometimes busy during january , and it is sometimes warm in november . French sentences 0 to 10: new jersey est parfois calme pendant l' automne , et il est neigeux en avril . les états-unis est généralement froid en juillet , et il gèle habituellement en novembre . california est généralement calme en mars , et il est généralement chaud en juin . les états-unis est parfois légère en juin , et il fait froid en septembre . votre moins aimé fruit est le raisin , mais mon moins aimé est la pomme . son fruit préféré est l'orange , mais mon préféré est le raisin . paris est relaxant en décembre , mais il est généralement froid en juillet . new jersey est occupé au printemps , et il est jamais chaude en mars . notre fruit est moins aimé le citron , mais mon moins aimé est le raisin . les états-unis est parfois occupé en janvier , et il est parfois chaud en novembre . Implement Preprocessing Function Text to Word Ids As you did with other RNNs, you must turn the text into a number so the computer can understand it. In the function text_to_ids(), you'll turn source_text and target_text from words to ids. However, you need to add the <EOS> word id at the end of target_text. This will help the neural network predict when the sentence should end. You can get the <EOS> word id by doing: target_vocab_to_int['<EOS>'] You can get other word ids using source_vocab_to_int and target_vocab_to_int. def text_to_ids(source_text, target_text, source_vocab_to_int, target_vocab_to_int): """ Convert source and target text to proper word ids :param source_text: String that contains all the source text. :param target_text: String that contains all the target text. :param source_vocab_to_int: Dictionary to go from the source words to an id :param target_vocab_to_int: Dictionary to go from the target words to an id :return: A tuple of lists (source_id_text, target_id_text) """ # TODO: Implement Function source_id_text = [[source_vocab_to_int[word] for word in sentence.split()] \ for sentence in source_text.split('\n')] target_id_text = [[target_vocab_to_int[word] for word in sentence.split()] + [target_vocab_to_int['<EOS>']] \ for sentence in target_text.split('\n')] return source_id_text, target_id_text """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_text_to_ids(text_to_ids) Tests Passed Preprocess all the data and save it Running the code cell below will preprocess all the data and save it to file. """ DON'T MODIFY ANYTHING IN THIS CELL """ helper.preprocess_and_save_data(source_path, target_path, text_to_ids) Check Point This is your first checkpoint. If you ever decide to come back to this notebook or have to restart the notebook, you can start from here. The preprocessed data has been saved to disk. import problem_unittests as tests """ DON'T MODIFY ANYTHING IN THIS CELL """ import numpy as np import helper (source_int_text, target_int_text), (source_vocab_to_int, target_vocab_to_int), _ = helper.load_preprocess() Check the Version of TensorFlow and Access to GPU This will check to make sure you have the correct version of TensorFlow and access to a GPU """ DON'T MODIFY ANYTHING IN THIS CELL """ from distutils.version import LooseVersion import warnings import tensorflow as tf from tensorflow.python.layers.core import Dense # Check TensorFlow Version assert LooseVersion(tf.__version__) >= LooseVersion('1.1'), 'Please use TensorFlow version 1.1 or newer' print('TensorFlow Version: {}'.format(tf.__version__)) # Check for a GPU if not tf.test.gpu_device_name(): warnings.warn('No GPU found. Please use a GPU to train your neural network.') else: print('Default GPU Device: {}'.format(tf.test.gpu_device_name())) TensorFlow Version: 1.1.0 Default GPU Device: /gpu:0 Build the Neural Network You'll build the components necessary to build a Sequence-to-Sequence model by implementing the following functions below: model_inputs process_decoder_input encoding_layer decoding_layer_train decoding_layer_infer decoding_layer seq2seq_model Input Implement the model_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders: Input text placeholder named "input" using the TF Placeholder name parameter with rank 2. Targets placeholder with rank 2. Learning rate placeholder with rank 0. Keep probability placeholder named "keep_prob" using the TF Placeholder name parameter with rank 0. Target sequence length placeholder named "target_sequence_length" with rank 1 Max target sequence length tensor named "max_target_len" getting its value from applying tf.reduce_max on the target_sequence_length placeholder. Rank 0. Source sequence length placeholder named "source_sequence_length" with rank 1 Return the placeholders in the following the tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) def model_inputs(): """ Create TF Placeholders for input, targets, learning rate, and lengths of source and target sequences. :return: Tuple (input, targets, learning rate, keep probability, target sequence length, max target sequence length, source sequence length) """ # TODO: Implement Function inputs = tf.placeholder(tf.int32, [None, None], 'input') targets = tf.placeholder(tf.int32, [None, None]) learning_rate = tf.placeholder(tf.float32, []) keep_prob = tf.placeholder(tf.float32, [], 'keep_prob') target_sequence_length = tf.placeholder(tf.int32, [None], 'target_sequence_length') max_target_len = tf.reduce_max(target_sequence_length) source_sequence_length = tf.placeholder(tf.int32, [None], 'source_sequence_length') return inputs, targets, learning_rate, keep_prob, target_sequence_length, max_target_len, source_sequence_length """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_model_inputs(model_inputs) Tests Passed Process Decoder Input Implement process_decoder_input by removing the last word id from each batch in target_data and concat the GO ID to the begining of each batch. def process_decoder_input(target_data, target_vocab_to_int, batch_size): """ Preprocess target data for encoding :param target_data: Target Placehoder :param target_vocab_to_int: Dictionary to go from the target words to an id :param batch_size: Batch Size :return: Preprocessed target data """ # TODO: Implement Function go = tf.constant([[target_vocab_to_int['<GO>']]]*batch_size) # end = tf.slice(target_data, [0, 0], [-1, batch_size]) end = tf.strided_slice(target_data, [0, 0], [batch_size, -1], [1, 1]) return tf.concat([go, end], 1) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_process_encoding_input(process_decoder_input) Tests Passed Encoding Implement encoding_layer() to create a Encoder RNN layer: Embed the encoder input using tf.contrib.layers.embed_sequence Construct a stacked tf.contrib.rnn.LSTMCell wrapped in a tf.contrib.rnn.DropoutWrapper Pass cell and embedded input to tf.nn.dynamic_rnn() from imp import reload reload(tests) def encoding_layer(rnn_inputs, rnn_size, num_layers, keep_prob, source_sequence_length, source_vocab_size, encoding_embedding_size): """ Create encoding layer :param rnn_inputs: Inputs for the RNN :param rnn_size: RNN Size :param num_layers: Number of layers :param keep_prob: Dropout keep probability :param source_sequence_length: a list of the lengths of each sequence in the batch :param source_vocab_size: vocabulary size of source data :param encoding_embedding_size: embedding size of source data :return: tuple (RNN output, RNN state) """ # TODO: Implement Function embed = tf.contrib.layers.embed_sequence(rnn_inputs, source_vocab_size, encoding_embedding_size) def lstm_cell(): lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size) return tf.contrib.rnn.DropoutWrapper(lstm, keep_prob) stacked_lstm = tf.contrib.rnn.MultiRNNCell([lstm_cell() for _ in range(num_layers)]) # initial_state = stacked_lstm.zero_state(source_sequence_length, tf.float32) return tf.nn.dynamic_rnn(stacked_lstm, embed, source_sequence_length, dtype=tf.float32) # initial_state=initial_state) """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_encoding_layer(encoding_layer) Tests Passed Decoding - Training Create a training decoding layer: Create a tf.contrib.seq2seq.TrainingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_train(encoder_state, dec_cell, dec_embed_input, target_sequence_length, max_summary_length, output_layer, keep_prob): """ Create a decoding layer for training :param encoder_state: Encoder State :param dec_cell: Decoder RNN Cell :param dec_embed_input: Decoder embedded input :param target_sequence_length: The lengths of each sequence in the target batch :param max_summary_length: The length of the longest sequence in the batch :param output_layer: Function to apply the output layer :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing training logits and sample_id """ # TODO: Implement Function helper = tf.contrib.seq2seq.TrainingHelper(dec_embed_input, target_sequence_length) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_train_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) # for tensorflow 1.2: # dec_train_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_summary_length) return dec_train_logits # keep_prob/dropout not used? """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_train(decoding_layer_train) Tests Passed Decoding - Inference Create inference decoder: Create a tf.contrib.seq2seq.GreedyEmbeddingHelper Create a tf.contrib.seq2seq.BasicDecoder Obtain the decoder outputs from tf.contrib.seq2seq.dynamic_decode def decoding_layer_infer(encoder_state, dec_cell, dec_embeddings, start_of_sequence_id, end_of_sequence_id, max_target_sequence_length, vocab_size, output_layer, batch_size, keep_prob): """ Create a decoding layer for inference :param encoder_state: Encoder state :param dec_cell: Decoder RNN Cell :param dec_embeddings: Decoder embeddings :param start_of_sequence_id: GO ID :param end_of_sequence_id: EOS Id :param max_target_sequence_length: Maximum length of target sequences :param vocab_size: Size of decoder/target vocabulary :param decoding_scope: TenorFlow Variable Scope for decoding :param output_layer: Function to apply the output layer :param batch_size: Batch size :param keep_prob: Dropout keep probability :return: BasicDecoderOutput containing inference logits and sample_id """ # TODO: Implement Function start_tokens = tf.constant([start_of_sequence_id]*batch_size) helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(dec_embeddings, start_tokens, end_of_sequence_id) decoder = tf.contrib.seq2seq.BasicDecoder(dec_cell, helper, encoder_state, output_layer) dec_infer_logits, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) # for tensorflow 1.2: # dec_infer_logits, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder, maximum_iterations=max_target_sequence_length) return dec_infer_logits """ DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE """ tests.test_decoding_layer_infer(decoding_layer_infer)